In one embodiment, several reduced order models (ROMs), each at a different scale, are developed to create a multiscale ROM for a given system, which can include one or more integrated circuits (ICs), for simulating thermal responses (e.g., heating in the ICs and the system) as a result of usage scenarios of the IC and system (in which power is applied on the IC and in the system) over time. An IC, package and system can be thermally characterized to determine transient temperature response to a power source in each IC; this characterization can use simulations of the power source to simulate temperature responses at points in the IC, package and system, and these responses provide a set of thermal training data. An algorithm generates a linear time-invariant (LTI) state-space thermal macromodel or ROM at each of the different scales on-chip and in system, from the set of thermal step response training data, and combines them into a multiscale ROM, which can be distributed and solved on many CPUs. When the multiscale ROM is used, power accounting can be used across the scales to conserve power. On IC symmetry and inverse heat transfer properties can be used to reduce the amount of thermal characterization operations.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
A computer-implemented method includes obtaining a model representing a physical object, accessing a discretized yield surface having a plurality of data points representing a yield surface of one or more materials of the physical object, each data point corresponding to a point in a stress space and associated with a yield surface normal vector precomputed for the discretized yield surface, and calculating a stress at an integration point of the model to simulate the physical object under a load. The method further includes identifying one of the plurality of data points of the discretized yield surface that corresponds to the stress calculated in the stress space, determining plastic deformation is occurring based on the stress calculated and the one data point identified, and adjusting the stress according to a normal vector associated with the one data point identified to simulate plastic deformation of the physical object under the load
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
3.
System and method of generating mesh representing flow domain
A system and method generates a mesh representing a flow domain around a surface of a physical object. The system receives an initial mesh representing the flow domain. The initial mesh has a corner vertex corresponding to a corner grid point of a grid in a parameterized space. The system updates the corner grid point to obtain an updated grid in the parameterized space according to angles formed by boundary edges and a border edge of the initial mesh. The system determines an updated mesh from the initial mesh according to the updated grid to reduce a difference between the angles formed by the boundary edges and the border edge. The system generates the mesh representing the flow domain for simulating a physical characteristic of the physical object.
Coupling of physics solutions (e.g., a first physics solution and a second physics solution) in a multi-physics simulation that includes non-conformal spaces can use extrapolation to determine data values for unmapped nodes by computing extrapolated values based on both mapped values from mapped nodes and data for unmapped nodes. The extrapolated values for an unmapped node are computed using a set of equations that include mapped values from nearby mapped nodes and data for nearby unmapped nodes. The methods and systems can provide improved computational efficiency by reducing the amount of memory required for the computations.
A computer-implemented method for adaptive mesh refinement includes generating a first mesh for a first layer of a stacked model representing a 3D integrated circuit, the first mesh including a first grid for the first layer, performing a simulation for the stacked model using the first mesh to obtain first results, calculating an interpolated value for a particular grid node according to the first results, the particular grid node corresponding to a node of the first grid in the grid hierarchy. The method further includes generating a second mesh for the layer of the stacked model, the second mesh including a second grid for the first layer, performing a simulation for the stacked model using the second mesh to obtain a simulated value of the particular grid node, comparing the interpolated and simulated value, and generating a refined mesh for the first layer including a refined grid.
Computational fluid dynamics simulations of objects with many similar but distinct flow boundary regions on a larger surface can use an approach that creates geometries and meshes for the larger surface (e.g., a first mesh) independently of geometries and meshes for the distinct flow boundary regions (second mesh). The second mesh can be imprinted onto the first mesh prior to a CFD simulation, and if a revision to the design of the regions is desired, a revised mesh for the regions (based on a revised geometry for the regions) can be imprinted on the first mesh without having to redo the first mesh. Thus, revisions of the design can avoid recreating the first mesh.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes analyzing a set of simulation inputs to determine a first set of computing resources for performing a simulation, and starting the simulation with the first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.
Systems and methods calculate Voronoi cells in a space subdivided into octants represented by an octree. Calculation of a Voronoi cell for a given seed point starts with the neighboring octants by determining the bisectors between the neighboring seed points and given seed point as well as the vertices where the bisectors meet. Additional octants are identified that (i) are adjacent to a neighboring octant and (ii) include any portion of the space that is no farther from a vertex than [(the distance between the vertex and the given seed point)+(additional distance ε)], such additional octants are added to the set of neighboring octants, and the sets of bisectors and vertices are updated using robust geometric predicates. This repeats until no additional octants are identified, and the bisectors and vertices are (optionally compressed, then) output as the Voronoi cell. Multiple cells can be determined in parallel, then combined into a Voronoi mesh.
max. A base function is determined that outputs magnitudes across the range of frequencies. A correction function is determined that outputs magnitudes across the range of frequencies. The base function is combined with the correction function to generate a circuit behavior model that provides magnitudes across the range of frequencies. Behavior of the electrical circuit is simulated using the circuit behavior model.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 119/06 - Power analysis or power optimisation
10.
Determination of heat source absorptivity and penetration depth in additive manufacturing melt pools
Computer-implemented systems and methods are described herein for determining additive manufacturing parameters in a simulation. Input data regarding a product to be generated via additive manufacturing and a beam diameter are received. Based on the input data, a characteristic dimension is determined. The beam diameter is normalized based on the characteristic dimension. Additive manufacturing parameters, such as penetration depth and absorptivity, are determined based on the normalized beam diameter and experimentally-determined trends. The manufacturing of the product is then simulated according to the additive manufacturing parameters.
Provided is an improved method, system, and computer program product to implement simulation for photonic devices. A composite, multi-domain simulation model is disclosed, with connected domain-specific representations that allow the use of the most relevant simulator technology for a given domain. The model has external connection points either expressed as actual ports or virtual ones, embodied by simulator API calls in the model.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
12.
Virtual testing of autonomous driving systems with thermal cameras
In one embodiment, use of virtual thermal cameras in simulations systems can rapidly increase the deployment of vehicles with autonomous driving systems that include thermal cameras as a part of the sensor systems that detect people, other cars, etc. This use can be achieved by calibrating virtual thermal cameras using a virtual object or scenes with predetermined temperatures of objects (humans) in the scenes. The virtually calibrated thermal camera can then be used in virtual simulations of driving scenarios to generate learning data, such as virtual thermal images, to test autonomous driving systems and autonomous driving algorithms. The calibration can use a temperature conversion process, and the generation of the learning data can also use this temperature conversion process that includes the addition of noise, represented by temperature values.
Methods and systems for improved simulation of thermal characterization and thermal modeling of devices, such as smart phones, are described. In one embodiment, a method can characterize center, edge, and corner thermal decay behavior at different locations on a simulated IC. For each location, near and far field thermal effects are captured at the same time. A simulation system can generate a steady state thermal decay curve for each selected location that shows how the temperature changes with distance to a heat source. The system can then use a set of location dependent thermal decay curves to compute, based on an inputted power profile for the IC, a steady state thermal profile of the IC.
A method and apparatus of a device of predicting a heat map for a die model is described. In an exemplary embodiment, the device receives a die model for representing the die and a boundary condition applicable to the die, the die model including dimensions of the die. In addition, the device may include scaling the dimensions of the die model and the boundary condition, wherein the dimensions of the die model are scaled to a target die size, the scaled dimensions including a scaled die thickness. Furthermore, each trained die model including a die surface of the target die size, different trained die models having different die thickness, the plurality of trained die models trained under a plurality of boundary conditions, each trained die model having a plurality of decay curves associated with the plurality of boundary conditions. The method may additionally include determining a relationship between the scaled boundary condition and the plurality of boundary conditions. In addition, the method may include determining a decay curve for the die model according to a combination of decay curves associated with the plurality of boundary conditions for the one or more trained die models using the relationship. Furthermore, the method may include generating the die heat map based on the decay curve determined for the die model.
A software tool used to assess and/or simulate an integrated circuit (“IC”) layout employs heterogeneous resistance extraction and/or meshing, to combine advantages of a relatively compact parasitics file size and robustness for effective simulation and electrical performance analysis. In one embodiment, filters are automatically applied (or are manually specified by a user) to select structures, layers, geographic regions, nets or other areas of a design which will receive additional extraction and/or meshing scrutiny, with the remainder of the design being subjected to default practices. For example, software can be scripted (or rule driven) to perform 1D meshing for in-layer electrical pathways while providing 2D, 3D, or other complex meshing for vias, contact pads, device instances, selected nets and/or other structures. Techniques can be applied for reassessing impact of structural redesign, and reports and/or visualization can be generated, each enhanced by the use of heterogeneous meshing.
G06F 30/392 - Floor-planning or layout, e.g. partitioning or placement
G06F 119/02 - Reliability analysis or reliability optimisationFailure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
16.
Methods and systems for predicting layer-wise physical behaviors in additive manufacturing
A three-dimensional model (i.e., MCAD model) for additively manufacturing a physical object with a laser is received in a computer system. The laser is controlled or driven by a set of instructions. The instructions specify scan properties for scanning each of multiple deposit layers during the additive manufacturing. Two-dimensional (2D) maps representing the scan properties for the deposit layer are generated. Each 2D map includes values in grid points representing the deposit layer. Each 2D map corresponds to a respective scan property for the deposit layer. A trained machine learning model is invoked to predict a physical behavior of a layer of the physical object based on the 2D maps as an input. The input further includes a state map showing a state of an immediately prior deposit layer. The layer of the physical object corresponds to the deposit layer.
A CAD method for capacitance extraction includes decomposing a semiconductor structure model representing a three-dimensional, into virtual layers. Each virtual layer has polygons corresponding to conductors in the semiconductor structure model. The semiconductor structure model or includes a geometric point. The method includes creating spatial indexes respectively for the virtual layers. Each spatial index indicates respectively for each spatial index cell of a corresponding virtual layer at least one candidate conductor of the corresponding virtual layer. The method includes creating optimal transition squares for the geometric point respectively for one or more consecutive virtual layers according to spatial indexes of the one or more consecutive virtual layers. The geometric point is located in one of the one or more consecutive virtual layers. The optimal transition squares are constrained by one or more candidate conductors of the one or more consecutive virtual layers. The method includes creating an optimal transition cube for the geometric point considering the one or more candidate conductors based on the optimal transition squares. The method includes calculating capacitance of the three-dimensional circuit using surfaces of the optimal transition cube.
Systems and methods are provided for generating a geometric shape. A mesh is defined that includes a plurality of mesh nodes. A plurality of control points are defined based on the mesh. The control points and mesh nodes are related through a differentiable function. A neural network is applied to the mesh. The neural network predicts one or more physical fields of the mesh based on the mesh and a parameter. A plurality of gradients of an objective function is calculated based on the one or more physical fields and a position of the plurality of control points. The position of one or more of the plurality of control points is moved based on the plurality of gradients. The movement induces a deformation of the mesh.
A model representing a physical object in a shape optimization according to a design objective, and a control point for altering a shape of the physical object are received. The shape is defined by a set of nodes in the model. Sibling models are generated from the model according to a perturbation scheme. The control point is perturbed with respective perturbed values for the sibling models. Each sibling model contains nodal location changes for the nodes. The nodal location changes are determined based on a respective shape function formulated according to a respective perturbed value at the control point and one or more simulated physical behaviors of the model. The model is updated to have an optimal value for the control point. The optimal value is identified from a relationship according to the design objective. The relationship correlates a physical characteristic of the sibling models to the respective perturbed values.
A generative machine learning model, such as a convolutional neural network (CNN), can be trained with solutions from a topology optimization solver for a solution for a topology of a set of structures so that the generative machine learning model can generate a plurality of alternative designs for a structure that are alternative topology optimizations (for the structure) for a set of initial setup parameters. The generative model when being trained includes a generative network and a discriminator network. The generative model can be trained using outputs from a CNN autoencoder for densities and a CNN autoencoder for strain energies.
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Computer-implemented devices, systems, and methods for physics-aware smart meshing are described. In various embodiments, physical aware smart meshing may include, or refer to, the generation of a dynamically sized mesh for a computer model based on one or more estimated physical fields corresponding to the computer model. The disclosed analysis techniques can include a series of steps including, for example, decomposing models, determining block properties, scaling from an original domain to a scaled domain, estimating physical fields, scaling from the scaled domain to the original domain, superpositioning of physical fields, and generating dynamically sized meshes. In various embodiments, the dynamically sized mesh is generated in an efficient and accurate manner without needing solve/adapt iterations.
Low-level test cases for safety-critical applications are automatically created and executed. A set of low-level test cases for a software design model is created by using both a non-qualified test case creation tool and a qualified model coverage analysis tool. The created set of low-level test cases is verified to cover low-level requirements for the software model with respect to coverage criteria. A set of high-level test cases for the requirements above the software design model and the set of low-level test cases are executed on a target architecture, based on the source code generated from the software design model, using a qualified code generator. The set of high-level test cases and the set of low-level test cases achieve coverage of the source code as the qualified code generator preserves coverage from model to code.
According to one aspect of this disclosure, time domain information can be associated with switching probabilities and propagated through a simulated circuit during a static timing analysis to provide finer grain details for use by a power analysis of the simulated circuit (such as a dynamic voltage drop analysis or simulation). Signal correlations may also be propagated with the associated time domain information to provide further finer details for use in power analysis. A method according to one embodiment can include: determining, for each input pin of an aggressor element, a set of one or more time dependent input switching probabilities during a set of one or more corresponding timing windows in a simulated representation of a circuit; determining, from each time dependent input switching probability of each timing window, a set of time dependent output switching probabilities at an output pin of the aggressor element; and performing a power analysis for one or more victim circuit elements that are coupled to the aggressor element, the power analysis based on the set of time dependent output switching probabilities at the output pin of the aggressor element.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Systems and methods use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network. The trained predictor can be used to determine a temperature rise and then can be appended to a system level thermal profile of the IC to generate a detailed thermal profile of the IC.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
A model representing a physical object is received. The model contains a pilot node, one or more surface nodes, and a constraint for coupling displacements/movements of the pilot node with the one or more surface nodes via a set of constraint equations. The pilot node is subject to a condition that restricts node swapping for resolving node dependency in elimination method. An internal node is created based on the pilot node. The internal node and the pilot node occupy a same location initially. The internal node and the one or more surface nodes are constrained via the set of constraint equations. The model is modified with the internal node and a numerical spring connecting the pilot node and the internal node. The numerical spring is configured for limiting relative movements between the pilot node and the internal node. Physical behaviors of the physical object are simulated using the modified model.
G06F 119/14 - Force analysis or force optimisation, e.g. static or dynamic forces
26.
Systems and methods for high-speed computationally efficient operation and training of machine learning-based simulation models characterizing a physical system
Systems and methods are provided for a computer-implemented method for simulating operation of a physical system represented by a plurality of mesh cells. A plurality of pre-simulations of the physical system are performed, where a pre-simulation determines an output metric value for each mesh cell based on a plurality of input metric values. A dimension reduction is performed on results of the pre-simulations to generate condensed training data, and a simulation model is generated using a machine learning algorithm to train a plurality of sub-models for simulating output metric values for the plurality of mesh cells based on the condensed training data. The simulation model is configured to provide simulation inputs to the trained sub-models to generate an output metric value for each of the plurality of mesh cells.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
27.
Capacitance extraction systems based on machine learning models
Extraction of capacitance values from a design of an electrical circuit can use a set of trained neural networks to generate extracted capacitance values from the circuit using a representation of the Green's function. A method can include the following operations: storing a machine learning model that includes a trained set of one or more neural networks that have been trained to calculate a representation of a Green's function to extract capacitance values from a design of a circuit having a set of conductors; applying, to the machine learning model, a set of inputs representing the set of conductors and their surrounding dielectric materials; encoding the set of inputs through a trained encoder to generate a latent space representation; calculating the values of the Green's function from the latent space representation through a dedicated trained neural network; and calculating the values of the gradient of the Green's function from the latent space representation through another dedicated trained neural network.
A specification for a semi-conductor chip is received. The specification specifies a set of photomasks associated with a metal layer of the semi-conductor chip. Multiple portions of an area of the metal layer are identified. A respective image is generated for each portion of the area based on the photomasks. A respective drawn density of metal wires for each portion of the area is calculated. A trained machine learning model is invoked to predict a respective silicon density of metal wires for each respective portion of the area based on an image and a drawn density for the respective portion of the area. A silicon density for the area of the metal layer is calculated based on a combination of predicted silicon densities for the multiple portions of the area. The combination is based on an average value of the predicted silicon densities for the multiple portions of the area.
The disclosed embodiments include a simulation system that guides, through a user interface, a creation of a set of reduced order models (ROMs) with compatible macros to ensure the creation of a set of computations in the simulation is compatible at execution time. The macros can provide a macro based workflow, where the macros evaluate the ROMs in the workflow. The user interface can restrict the set of macros to those that are compatible with a selected set of ROMs. In one embodiment, the set of ROMs can produce a customizable set of outputs using a user defined sequence of ROMs that are associated with corresponding selected macros to simulate a physical system. The set of ROMs and their compatible macros can be encapsulated into a single unit such as a Functional Mock-Up Unit file.
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 111/20 - Configuration CAD, e.g. designing by assembling or positioning modules selected from libraries of predesigned modules
30.
Systems and methods for radar signature modeling using a rotating range profile reduced-order model
Systems and methods are provided for generating a radar model for a target object. In embodiments, a target simulation model is received that represents one or more physical aspects of a target object, an environment simulation model is received that represents one or more physical aspects of an environment object, and a target distance parameter is received that identifies a reference distance between the target object and a radar system to be simulated. A simulation model is generated based, at least in part, on the target simulation model, the environment simulation model, and the reference distance, and further based on a target aspect angle that identifies an angular position of the target object in relation to the radar system. Interaction of the radar system with the target object and the environment object is simulated using the simulation model, and results of the simulation are used to generate a range profile for the target object at the target aspect angle, wherein the range profile identifies a radar return strength for the reference distance. The target aspect angle is then incremented, and the operations are repeated until range profiles are generated for the target object at a plurality of target angles amounting to a 360 degree rotation of the target object. The range profiles at the plurality of target angles are then accumulated to generate the radar model for the target object.
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
G01S 7/295 - Means for transforming co-ordinates or for evaluating data, e.g. using computers
A system receives a layout representation of a metasurface, the metasurface including a number of scatterers arranged in a spatial order, each scatterer having one of a number of geometries. The system identifies one or more consecutive ones of the scatterers in the spatial order. The system identifies a sequence representing consecutive ones of the geometries, the sequence being associated with one or more consecutive ones of the scatterers in the spatial order. The system associates a two-dimensional coordinate to the sequence, the two-dimensional coordinate corresponding to a position in the metasurface where the sequence starts. The system generates an output layout file including a reference to the sequence and the associated two-dimensional coordinate.
A method is disclosed for augmenting the SBR model used in EM field simulation by modeling the specular coherent and diffuse incoherent components of the field scattered by rough surfaces using statistical characteristics of surface roughness. For each projected ray-tube footprint, the magnitude of the coherent radiated field is attenuated with a scalar factor, while the incoherent radiated field is modulated by a random magnitude and phase. Both corrections are based on the statistical characteristics of surface roughness. Multiplying the incoherent field with a randomly generated phase renders it in a mathematically coherent form, which allows the method to generate a statistically viable instance of the total (i.e. coherent plus incoherent) field scattered by a rough surface. The results reproduce the field and power statistics (i.e. mean and variance) observed from direct SBR simulations using an ensemble of explicitly rendered rough surface geometry models, with a significant reduction in computation.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06F 30/15 - Vehicle, aircraft or watercraft design
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Methods, machine readable media and systems for evaluating, through one or more simulations, the leakage of sensitive data in an integrated circuit, such as cryptographic data or keys, are described. The embodiments can use machine learning models, such as one or more neural networks to generate one or more leakage related scores for each portion in a set of portions of the cryptographic data. In one embodiment, leakage data associated the first set of POIs with one or more neural networks is processed by the one or more neural networks to identify the POIs that leak the most and determine one or more scores for each portion in the set of portions of the cryptographic data.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
34.
Real time, compact, dynamic, transfer learning models
A method and apparatus of a device for generating a fusion model for a physical system is described. In an exemplary embodiment, the device receives low-fidelity input data and low-fidelity output data that represent a low-fidelity measurement of a physical system. In addition, the device trains a first model to predict the low-fidelity output data using the low-fidelity input data. Furthermore, the device receives high-fidelity input data and high-fidelity output data that represent a high-fidelity measurement of the physical system. The device additionally invokes the first model with the high-fidelity input data to generate predicted low-fidelity data. The device further trains a second model to predict the high-fidelity output data using the high-fidelity input data augmented with the predicted low-fidelity data. In addition, the device creates a fusion model for the physical system based on the first model and the second model, the first model and the second model to receive input to the fusion model, the second model to receive output from the first model, and output of the fusion model corresponding to output of the second model.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
35.
SYSTEMS AND METHODS FOR GENERATING SUPPORT STRUCTURES OF A MECHANIC OBJECT
Methods and apparatuses for generating support structures for a mechanical object are disclosed. One or more load scenarios are obtained to identify a first stress heatmap to represent stress characteristics of a first layer of a mechanical object based on the one or more load scenarios. A mesh of support structures is determined for the object based on the stress heatmap. A design of the mechanical object is updated with the support structures according to the mesh for manufacturing the mechanical object.
Methods, systems and media for simulating or analyzing voltage drops in a power distribution network can use an incremental approach to define a portion of a design around a victim to capture a sufficient collection of aggressors that cause appreciable voltage drop on the victim, and then an incremental simulation of just the portion can be performed rather than computing simulated voltage drops across the entire design. This approach can be both computationally efficient and can limit the size of the data used in simulating dynamic voltage drops in the power distribution network. Multiple different portions can be simulated separately in separate processing cores or elements. In one embodiment, a system can provide options of user selected constraints for the simulation to provide better accuracy or use less memory. Better accuracy will normally use a larger set of aggressors for each victim at the expense of using more memory.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 119/06 - Power analysis or power optimisation
37.
Methods and systems for creating mechanical computer-aided design model to include deformation
A mechanical computer-aided design (MCAD) model representing an object is received. The MCAD model contains a surface with a face defined therein. Deformation of the face is obtained in a simulation based on the MCAD model. The surface is defined by Non-Uniform Rational Basis Spline patches and mapped to a two-dimensional (2-D) parametric grid. The 2-D grid contains a first group of grid points representing the face and a second group of grid points representing outside of the face. The deformation of the face is applied to the first group of grid points. The deformation is extrapolated to the second group of grid points. An updated MCAD model representing a deformed object is generated using the 2-D grid associated with the applied deformation and the extrapolated deformation.
A computer-implemented method for meshing a model of a physical electro-magnetic assembly is disclosed. The method includes separating the base mesh of the model into two domains and freezing the boundary between these domains. Each domain is then sent for mesh refinement by separate computer processors. Each computer processor generates a refined mesh of the respective domain without communication between processors. Two-way boundary mesh mapping is then performed, resulting in a global conformal mesh. Surface recovery and identity assignment are then performed by separate computer processors in parallel for each domain, without communication between processors. Related apparatus, systems, techniques, methods and articles are also described.
Methods, machine readable media and systems for evaluating, through one or more simulations, the leakage of sensitive data in an integrated circuit, such as cryptographic data or keys, are described. The embodiments can use machine learning models, such as one or more neural networks to generate one or more leakage related scores for each portion in a set of portions of the cryptographic data. In one embodiment, leakage data associated the first set of POIs with one or more neural networks is processed by the one or more neural networks to identify the POIs that leak the most and determine one or more scores for each portion in the set of portions of the cryptographic data.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
40.
Method and system to generate thermophysical property for finite element based solvers
In one embodiment, a system derives non-equilibrium thermophysical values for phase property changes of a material from equilibrium thermophysical values of the material for a manufacturing process which involves heating and/or cooling of the material (such as an additive manufacturing, 3D printing, welding, or joining process). The system performs a simulation of the manufacturing process based upon the derived non-equilibrium and/or equilibrium thermophysical values. The system generates a set of results based on the simulation, the set of results indicating predicted physical properties of the material for the manufacturing process.
A global computer aided engineering (CAE) model representing an electronic product that contains solder joints and an individual detailed solder joint model are received. The solder joint model can include a solder ball, one or more metal pads, a portion of printed circuit board, and a portion of semiconductor chip component. The global CAE model includes locations of the solder joints to be evaluated in a drop test simulation. The solder joint model is replicated at each location to create a local CAE model via a geometric relationship between the global CAE model and the local CAE model. Simulated physical behaviors of the product under a design condition are obtained in a co-simulation using the global CAE model in a first time scale and the local CAE model in a second time scale. Simulated physical behaviors are periodically synchronized based on kinematic and force constraints.
G06F 30/20 - Design optimisation, verification or simulation
G06F 30/25 - Design optimisation, verification or simulation using particle-based methods
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
G06F 119/02 - Reliability analysis or reliability optimisationFailure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
G06F 119/22 - Yield analysis or yield optimisation
42.
COHESIVE ZONE-BASED CRITERIA FOR ADAPTIVITY DRIVEN DEBONDING ANALYSIS
The present disclosure relates to criteria for adaptive remeshing in debonding simulations. For example, one or more embodiments described herein include a computer-implemented method comprising determining, by a processor, a released energy, a damage parameter value, or a contact status of a contact element included within a first mesh that represents a region of physical objects simulated by a cohesive zone model. The computer-implemented method can also comprise remeshing, by the processor, a finite element associated with the contact element to generate a second mesh to represent the region based on the released energy, the damage parameter value, or the contact status to obtain physical characteristics associated with a debonding of the physical objects.
A system for simulating operation of a battery pack comprising a plurality of battery modules includes an electrical model configured to simulate electrical behavior of a respective battery module and a thermal model configured to simulate thermal behavior of the respective battery module. The electrical model provides outputs coupled as inputs to the thermal model, and the thermal model provides outputs coupled as inputs to the electrical model. A coolant model is configured to couple with the thermal model, the coolant model to simulate cooling of the respective battery module based on a temperature and a heat transfer coefficient associated with the respective battery module.
Machine assisted systems and methods for anisotropic polyhedral boundary layer adaptation that provides an anisotropic refinement of polyhedral prisms are described. The method can include operations: identifying a prismatic polyhedral cell as a parent cell within a polyhedral mesh representing an object of a physical system; generating a plurality of child cells for the parent cell based on non-overlapping child faces of a side face as a parent face, wherein a pair of mid-edge nodes for the side face are connected anisotropically for a child face edge of the non-overlapping child faces, the pair of mid-edge nodes are connected between the two base edges or between the two side edges; and refining the polyhedral mesh until a refinement level or a size limit is obtained, wherein the refining the polyhedral mesh includes the identification of the prismatic polyhedral cell and the generation of the plurality of child cells.
Data is received that includes a triangular mesh. Thereafter, a curvature aligned cross field is generated on the triangular mesh. The cross field is used to calculate a quad layout. A subdivision surface can be created using the quad layout as a control cage. Later, vertex locations in the control cage are determined to result in the subdivision surface being an approximation of the triangular mesh. The subdivision subsurface (or a portion thereof) can be visualized in a graphical user interface (e.g., as part of a CAD software application, etc.). Related apparatus, systems, techniques and articles are also described.
Machine assisted systems and methods for use in meshing a periodic pattern of a geometry that represents a physical structure in a simulation of the physical structure are described. The method can include operations: identifying a master region in a periodic pattern of a geometry that represents a physical structure in a simulation of the physical structure that has the periodic pattern; and generating a mesh based on the identified master region, the mesh including a non-planar boundary at a boundary between patterns in the periodic pattern, wherein the master region is only a portion of the representation of the physical structure.
Techniques for meshing a plurality of objects include determining, for at least a portion of the objects, a respective priority level. A mesh is then generated for each object. Thereafter, at least a portion of the meshes are sequentially imprinted to each other such that conflicts arising between meshes are resolved in favor of an object having a higher priority level. Related apparatus, systems, techniques and articles are also described.
Analysis of power supply noise in simulations of a design of a circuit can use per instance dynamic voltage drops (DVD) in timing analyses so that the simulated DVD values on a per victim cell basis can accurately guide the timing analysis on each victim instead of a global DVD for all victims during the timing analysis. In one embodiment, a method can: determine, during a power analysis simulation, a representation of an energy lost, during each switching window at each output of each victim cell, at one or more power supply rails of each of the victim cells in the set of victim cells due to aggressors in the design; and provide the representation of the energy lost separately for each victim cell to a timing analysis system. The representation can be a rectangle having a width defined by a switching window of a victim's output.
Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes performing a simulation with a first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.
Partial differential equations used to simulate physical systems can be solved, in one embodiment, by a solver that has been trained with a set of generative neural networks that operated at different resolutions in a solution space of a domain that defines the physical space of the physical system. The solver can operate in a latent vector space which encodes solutions to the PDE in latent vectors in the latent vector space. The variables of the PDE can be partially decoupled in the latent vector space while the solver operates. The domain can be divided into subdomains that are classified based on their positions in the domain.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06N 3/00 - Computing arrangements based on biological models
G06N 3/084 - Backpropagation, e.g. using gradient descent
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
51.
Methods and systems for identifying, filtering and classifying contact-pairs
A model (e.g., mechanical computer-aided design (MCAD) model) representing an assembly is displayed in a graphical user interface (GUI). The assembly contains at least a first body and a second body. The physics type of the model is determined in response to an assignment of a physics condition to the first body and/or the second body. Contact-pairs are detected in the model according to the determined physics type. The GUI is updated to indicate where the contact-pairs are located in the model for revision of the contact-pairs. Markers are shown in the GUI to represent corresponding contact-pairs. Markers are logically separated from bodies and faces in the model. The revision of the contact-pairs can be managed with a filtering mechanism either by bodies/faces or by markers.
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
52.
COMPUTATIONAL FLUID DYNAMICS (CFD) METHOD INCLUDING A KINETICS-BASED MODEL FOR SPECIES RESPONSE IN A FLAME FRONT
Embodiments are disclosed of a computer-implemented method. The method includes establishing a computational grid that includes a plurality of computational cells and describes a volume containing a combustible fluid mixture. The method identifies positions in the computational grid of a flame front propagating through the combustible fluid mixture, identifies a set of representative computational cells that can be used as a computational representation of the flame front, and applies a well-mixed-reactor model and a G-equation model to every computational cell within the set of representative computational cells to compute chemical results from combustion of the combustible fluid mixture in the flame front.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
Methods and systems for performing timing analysis during the design of a circuit are described. In one embodiment, a simulation system can generate an effective resistance value (or an impedance value based on the effective resistance value) for an instance and use the effective resistance value in a simulation to determine a minimum timing delay for the instance when only the instance switches during such simulations.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Data characterizing a fastener and a preload condition applied to the fastener are received in a computer system. A model representing a mechanism coupling a first portion and a second portion of the fastener is created. The first portion and the second portion are located respectively at either end of the fastener along a longitudinal axis of the fastener. The first portion and the second portion are axially displaceable along the axis towards each other via the mechanism and rotatably displaceable about the axis relative to each other via the mechanism. The model includes relative axial displacement and/or relative torsional displacement between the first portion and the second portion corresponding to the preload condition. Physical behaviors of the fastener applied with the preload condition is simulated based on the model.
Computationally efficient and accurate methods of fitting black box simulation data to obtain Linear Parametrically Varying models useful for design of thermal management systems are disclosed. The Linear Parametrically Varying models may be used in dynamic simulations to determine temperatures in thermal systems. A computer-implemented model is run at a plurality of fixed values of the scheduling parameter to obtain output responses to excitations of the input at the fixed values of the scheduling parameter. LTI representations are fit to the output responses, the LTI representations having state-space variables characterized by negative real poles. Coefficient matrices of the LTI representations are updated, and an LPV-ROM representation is generated.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 119/08 - Thermal analysis or thermal optimisation
56.
Techniques for solving additive manufacturing part analyses for powder bed fusion technologies
This disclosure provides modelling techniques for accurately and reliably analyzing printed parts. More specifically, a model with a powder representation may be used for the thermal analysis, the powder representation may then be removed from the model for performing the structural analysis. Although the powder representation is removed from the model for the structural analysis, the structural analysis still factors in the thermal effects of the powder on the part determined by the thermal analysis. The computer-based techniques of the current disclosure improve the functioning of a computer system as compared to conventional approaches by facilitating analyses (e.g., thermal, mechanical, design, and heat treatment analyses) that are more accurate, more efficient (e.g., faster, smaller memory requirements, etcetera), and/or have a reduced processing burden versus conventional approaches.
B29C 64/386 - Data acquisition or data processing for additive manufacturing
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
B22F 10/28 - Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM]
B29C 64/153 - Processes of additive manufacturing using only solid materials using layers of powder being selectively joined, e.g. by selective laser sintering or melting
A computer-implemented numerical simulation method (500) for predicting the flow of a fluid in a simulation domain by a deep learning model, comprising a step (510) of generating a mesh of the domain and, for each node i of the mesh, a step (520) of creating a position vector pi and an attribute vector Xi at a first iteration t; a step (530) of computing messages between the node i and all its neighbouring nodes by means of a recurrent artificial neural network (100); a step (540) of updating the attribute vector by means of said network, from the computed messages, giving a state of the attribute vector at a second iteration t+1; the sequence comprising the step (530) of computing messages and the step (540) of updating the attribute vector being carried out by applying a local operator and being repeated n times until a convergence is obtained, said method finally comprising a step (550) of interpreting the attribute vectors of all the nodes of the mesh as a physical field such as a velocity field or a pressure field.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
58.
Systems and Methods for Training and Simulation of Autonomous Driving Systems
Systems and methods are provided for simulating operation of an autonomous vehicle control system. Three dimensional multi-sensor data associated with a plurality of real-world drives in a sensor equipped vehicle is accessed. For a particular drive, the three dimensional multi-sensor data is reduced to a time series of two dimensional representations. The time series of two dimensional representations is classified into a sequence of states, where the sequence of states associated with the particular drive and the three dimensional multi-sensor data are stored in a computer-readable medium as a scenario. A query is received that identifies a state criteria, and the scenario is accessed based on the sequence of states matching the state criteria of the query. The three dimensional multi-sensor data of the scenario is provided to an autonomous driving system to simulate behavior of the autonomous driving system when faced with the scenario.
G06F 30/15 - Vehicle, aircraft or watercraft design
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G06F 18/2137 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
59.
METHOD AND APPARATUS FOR REDUCING CHEMICAL REACTION MECHANISMS
Method and apparatus for reducing chemical reaction mechanisms are disclosed. A method comprises obtaining data for one or more chemical species in a chemical reaction model from a database, the database including properties of the one or more chemical species; grouping the chemical species in a chemical reaction model into one or more isomer groups according to molecular properties of the chemical species; assigning a representative isomer to at least one isomer group; replacing, in one or more chemical reaction equations of the chemical reaction model, one or more groups of chemical species with a corresponding representative isomer; and executing the chemical reaction model by an apparatus to determine results.
Methods and apparatuses that generate a simulation object for a physical system are described. The simulation object includes a trained computing structure to determine future output data of the physical system in real time. The computing structure is trained with a plurality of input units and one or more output units. The plurality of input units include regular input units to receive input data and output data of the physical system. The output units include one or more regular output units to predict a dynamic rate of change of the input data over a period of time. The input data and output data of the physical system are obtained for training the computing structure. The input data represent a dynamic input excitation to the physical system over the period of time. And the output data represents a dynamic output response of the physical system to the dynamic input excitation over the period of time.
Methods, machine readable media and systems for a method to determine if a model of an integrated circuit (IC) includes a Trojan component are described. In one embodiment, a method can include the following operations: splitting the model of the IC into a plurality of tiles; simulating the IC with an elevated temperature of each tile to predetermined level; computing a temperature-dependent leakage power for each tile; and identifying a tile of the IC as including a Trojan component based on the temperature-dependent leakage power computed.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
A system and method of modeling flow of a vasculature in near real-time, is described. A vessel segment of the vasculature is modeled by a reduced order model, and a remainder of the vasculature is modeled by a 0D model. The reduced order model is generated using boundary conditions generated by a 0D model of the entire vasculature. Moreover, the reduced order model of the vessel segment and the 0D model of the remainder of the vasculature can be coupled to simulate flow that can be compared to actual flow measurements to personalize the 0D model of the remainder of the vasculature for a patient. Accordingly, a physician can update parameters of the personalized vascular system model to predict the effects of treatment protocols, including exercise or therapeutic substances, on the patient. Other embodiments are also described and claimed.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/02 - Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/0285 - Measuring phase velocity of blood waves
Low-level test cases for safety-critical applications are automatically created and executed. A set of low-level test cases for a software design model is created by using both a non-qualified test case creation tool and a qualified model coverage analysis tool. The created set of low-level test cases is verified to cover low-level requirements for the software model with respect to coverage criteria. A set of high-level test cases for the requirements above the software design model and the set of low-level test cases are executed on a target architecture, based on the source code generated from the software design model, using a qualified code generator. The set of high-level test cases and the set of low-level test cases achieve coverage of the source code as the qualified code generator preserves coverage from model to code.
A computer-implemented method for rich logging of simulation results is disclosed. The method includes running a simulation of a physical process, generating a log file entry with a contextual attribute about a state of the simulation, storing the log file entry in a database, receiving a query to the database from a client referencing a contextual attribute, generating a message from the database in response to the query, and sending the message to the client. Related apparatus, systems, techniques, methods and articles are also described.
Systems and methods are provided for a processor-implemented method for compressing, storing, and transmitting simulation data. The simulation data including a set of floating point data values is received, the simulation data characterizing simulated physical properties of a physical object. A first master data value is identified from the set to cluster one or more data values from the set as a first group of data values based on a comparison between a data value of the set and the first master data value. Compressed simulation data is transmitted, where the compressed simulation data includes a floating point representation of the first master data value and integer representations of other data values of the first group of data values.
H03M 7/24 - Conversion to or from floating-point codes
H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 48/08 - Access restriction or access information delivery, e.g. discovery data delivery
66.
APPLICATION OF nD-ROMs ON A RESTRICTED GEOMETRY FOR REAL-TIME TOPOLOGY OPTIMIZATION
Embodiments are described of a computer-implemented method. The method receives a geometry of a design space for a design of a physical object based on one or more design parameters and a design objective subjected to design constraints and performs a plurality of topology optimizations according to the design objective to generate two or more structure configurations within the design space of the geometry based on two or more sets of values of the design parameters. Each structure configuration corresponds to a respective set of values of the design parameters. The method then generates a ROM (reduced order model) to predict each of the two or more structure configurations from a corresponding set of the design parameters, receives a separate set of values of the design parameters, and invokes the ROM to predict a target structure configuration within the design space of the geometry from the separate set of values of the design parameters, the target structure configuration for building or manufacturing the physical object.
G06F 30/20 - Design optimisation, verification or simulation
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
67.
Method and system to implement a composite, multi-domain model for electro-optical modeling and simulation
Provided is an improved method, system, and computer program product to implement simulation for photonic devices. A composite, multi-domain simulation model is disclosed, with connected domain-specific representations that allow the use of the most relevant simulator technology for a given domain. The model has external connection points either expressed as actual ports or virtual ones, embodied by simulator API calls in the model.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
68.
Adaptive leakage impact region detection and modeling for counterfeit chips detection
A method in one embodiment creates a model of an authentic IC for use in comparisons with counterfeit ICs. The model can be created by determining a first or initial set of points of interest (POIs) on the simulated physical (e.g., gate level) layout and simulating side channel leakage from each POI and then expanding the size of the POI and repeating the simulation and comparing successive simulation results (between successive sizes of POIs for a given POI) to determine if a solution for the size of the POI has converged. The final POIs are then processed in a simulation that can use multiple payloads (e.g., cryptographic data) over the entire set of final POIs, and the resulting data set can be used to create the model.
Techniques are provided for the transfer of data (e.g. electromagnetic losses and temperature for an electro-thermal simulation) between domains (e.g. 2D regions, 3D regions) with dissimilar topologies, e.g. to represent a physical object. Meshes from 2D and 3D regions of respective simulation models involved in the data transfer are projected onto and through one another. For profile preserving (e.g., temperature, convection coefficients, mesh displacements, etc.) and conservative (e.g., force, mass, or thermal energy, etc.) data transfers, shape functions and area/volume fractions are used in mapping weight generation, respectively. These weights are later used to generate field values on the mesh of the target simulation model, using data from the mesh of the source simulation model. Related apparatus, systems, techniques and articles are also described.
Methods and systems for performing timing analysis during the design of a circuit are described. In one embodiment, a simulation system can generate an effective resistance value (or an impedance value based on the effective resistance value) for an instance and use the effective resistance value in a simulation to determine a minimum timing delay for the instance when only the instance switches during such simulations.
G06F 30/3315 - Design verification, e.g. functional simulation or model checking using static timing analysis [STA]
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 30/38 - Circuit design at the mixed level of analogue and digital signals
G06F 119/12 - Timing analysis or timing optimisation
71.
AUTOMATING EXTRACTION OF MATERIAL MODEL COEFFICIENTS FOR SIMULATIONS
Systems and methods for deriving material coefficient values from physical measurements of physical objects are described. These physical measurements can provide material test data. In one embodiment of a method described herein, the material test data can be used to calculate a first subset of material coefficients for use in one or more material models from material test data; the method can define a set of optimization parameters and define a set of relationships between a second subset of the material coefficients and the set of optimization parameters such that values of the material coefficients in the second subset of material coefficients can be calculated from the set of optimization parameters, wherein the first subset and the second subset include all of the material coefficients in the one or more material models and the set of optimization parameters have fewer parameters than a total number of coefficients in the first subset and the second subset; then the method can calculate optimum values of the optimization parameters to yield an optimum fit of the one or more models to the material test data, the optimum fit based on optimum material coefficients calculated from the calculated optimum values of the optimization parameters.
Systems and methods for simulating a circuit board design include receiving a printed circuit board design comprising an electronic component and a dielectric board, generating a first finite element model of the dielectric board independent of the electronic component, and generating a second finite element model for the electronic component. The method further includes combining the first finite element model with the second finite element model to obtain a final finite element model for the printed circuit board design.
Methods for modeling contact pairs in a model of a physical object include generating a contact pair including a contact surface and a target surface, where the contact pair further includes contact elements of the contact surface and the target surface, splitting the contact pair into contact sub-pairs along splitting boundaries, augmenting each contact sub-pair with contact elements from adjacent contact sub-pairs at the splitting boundaries, distributing the augmented contact sub-pairs to a plurality of parallel processors for finite element solutions of the contact sub-pairs, receiving the finite element solutions of the contact sub-pairs from the plurality of parallel processors, and combining the finite element solutions of the contact sub-pairs into finite element solutions of the contact pair.
Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network. The trained predictor can be used to determine a temperature rise and then can be appended to a system level thermal profile of the IC to generate a detailed thermal profile of the IC.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Methods and apparatuses that generate a simulation object for a physical system are described. The simulation object includes a trained computing structure to determine future output data of the physical system in real time. The computing structure is trained with a plurality of input units and one or more output units. The plurality of input units include regular input units to receive input data and output data of the physical system. The output units include one or more regular output units to predict a dynamic rate of change of the input data over a period of time. The input data and output data of the physical system are obtained for training the computing structure. The input data represent a dynamic input excitation to the physical system over the period of time. And the output data represents a dynamic output response of the physical system to the dynamic input excitation over the period of time.
A method in one embodiment creates a model of an authentic IC for use in comparisons with counterfeit ICs. The model can be created by determining a first or initial set of points of interest (POIs) on the simulated physical (e.g., gate level) layout and simulating side channel leakage from each POI and then expanding the size of the POI and repeating the simulation and comparing successive simulation results (between successive sizes of POIs for a given POI) to determine if a solution for the size of the POI has converged. The final POIs are then processed in a simulation that can use multiple payloads (e.g., cryptographic data) over the entire set of final POIs, and the resulting data set can be used to create the model.
The present invention relates to an apparatus and a method of analyzing durability of a drive line for a vehicle, and more particularly, to a durability analysis automation technology which automates a durability analysis process performed for verifying durability performance in a stage of designing a drive line to easily and rapidly verify durability performance of the drive line.
Systems and methods are provided. A physical measurement of the core loss increase associated with a physical deformation of a material of the device is obtained. A data structure describing a model of the device is accessed. A first edge of the model of the device associated with a physical deformation of the device is identified. A finite element mesh is generated to include a single layer mesh comprising a plurality of mesh elements associated with the first edge of the finite element mesh. A core loss value is assigned to each of the plurality of mesh elements. Each of the core loss values representative of the physical measurement of the core loss increase of the material as a result of the physical deformation of the material. An electromagnetic model is generated by performing a finite element analysis based on the finite element mesh and the single layer mesh.
A specification of a honeycomb based crash barrier including a cell is received in a computer system. The cell contains trapped air. A model representing the honeycomb based crash barrier is generated. The trapped air in the cell is represented as a compressible element, which is characterized by a force-deflection relationship between an air pressure in the cell and a crush-distance of a compression of the cell. Physical behaviors of the honeycomb based crash barrier are simulated using the model. The physical behaviors include numerical behaviors of the trapped air based on the force-deflection relationship.
An embodiment of a method can create a directed acyclic graph (DAG) from a programmer specified set of computation units to solve, in a computer program, physics based simulations of physical systems, and the DAG can be used to analyze and debug the computer program. In this method, the computer program can be created by automatically determining dependency relationships in the set of computation units and automatically schedule their execution. The method can also automatically allocate memory for the computation units.
A method and a system of generating a smooth spline surface from a quadrilateral mesh representing a physical object, is described. Edge points are added to the quadrilateral mesh to preserve a feature or a boundary. The edge points are added to an edge of the quadrilateral mesh having one or more irregular nodes representing a feature edge of the physical object and/or the edge points are added to an edge of the quadrilateral mesh extending between the feature edge and an irregular node. Knot interval vectors are extracted for each edge point and used to evaluate an irregular element having the edge points to generate the smooth spline surface. The smooth spline surface is highly continuous and can be displayed to model the physical object, modify a design of the physical object, or used directly in a simulation to evaluate the physical object. Other embodiments are also described and claimed.
Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes analyzing a set of simulation inputs to determine a first set of computing resources for performing a simulation, and starting the simulation with the first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.
Methods, systems and media for simulating or analyzing voltage drops in a power distribution network can use an incremental approach to define a portion of a design around a victim to capture a sufficient collection of aggressors that cause appreciable voltage drop on the victim, and then an incremental simulation of just the portion can be performed rather than computing simulated voltage drops across the entire design. This approach can be both computationally efficient and can limit the size of the data used in simulating dynamic voltage drops in the power distribution network. Multiple different portions can be simulated separately in separate processing cores or elements. In one embodiment, a system can provide options of user selected constraints for the simulation to provide better accuracy or use less memory. Better accuracy will normally use a larger set of aggressors for each victim at the expense of using more memory.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 119/06 - Power analysis or power optimisation
84.
Security-aware design with placement for power/emag assessment
Techniques for security vulnerability assessment of security-sensitive circuit designs are described. A placement of security-sensitive components of a design may be based on constraints related to how far apart a relevant set of security-sensitive components are allowed without consuming too much power and how to optimize the placement to minimize electromagnetic side-channel leakage or other security vulnerabilities. In one embodiment, a method may receive data that includes a representation of a design of an IC and may identify security-sensitive components of the design from the data. The method may determine a placement for the design based on constraints on a level of security vulnerabilities of the security-sensitive components and may perform a power simulation for the design based on the placement. The method may generate an assessment of the level of security vulnerabilities of the security-sensitive components based on the power simulation to adjust the placement for the design.
G06F 30/33 - Design verification, e.g. functional simulation or model checking
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 30/3308 - Design verification, e.g. functional simulation or model checking using simulation
A computer-implemented method for meshing a model of a physical electro-magnetic assembly is disclosed. The method includes separating the base mesh of the model into two domains and freezing the boundary between these domains. Each domain is then sent for mesh refinement by separate computer processors. Each computer processor generates a refined mesh of the respective domain without communication between processors. Two-way boundary mesh mapping is then performed, resulting in a global conformal mesh. Surface recovery and identity assignment are then performed by separate computer processors in parallel for each domain, without communication between processors. Related apparatus, systems, techniques, methods and articles are also described.
A specification for a semi-conductor chip is received. The specification specifies a set of photomasks associated with a metal layer of the semi-conductor chip. Multiple portions of an area of the metal layer are identified. A respective image is generated for each portion of the area based on the photomasks. A respective drawn density of metal wires for each portion of the area is calculated. A trained machine learning model is invoked to predict a respective silicon density of metal wires for each respective portion of the area based on an image and a drawn density for the respective portion of the area. A silicon density for the area of the metal layer is calculated based on a combination of predicted silicon densities for the multiple portions of the area. The combination is based on an average value of the predicted silicon densities for the multiple portions of the area.
A system and method are provided for simulating circuits that transmit bidirectional signals between some ports using simulators designed originally for electrical circuits and systems, that eliminate the need for different port interfaces. The system and method can be applied to simulate photonic circuits either standalone or integrated with electrical circuits and systems. In one method implemented by the system potential and flow representations, available for example in Verilog-A simulators, are used to create bidirectional signals on a single bus line to transmit optical signals. In another method implemented by the system, the system auto-configures each optical port type as left or right at runtime or during a pre-simulation initialization to allow for bidirectional signals with a single port interface.
Computer-implemented systems and methods are described herein for determining mechanical properties of an electronic assembly. An input specification for a model of the electronic assembly is received, wherein the input specification includes a compressible body and a surrounding component in the electronic assembly. A geometric interference between the compressible body and the surrounding component is identified. A displacement is generated for the compressible body to account for the geometric interference. A non-linear contact is then generated between the displaced compressible body and the surrounding component. The model is updated with the displacement and the non-linear contact. Then, a resulting force equilibrium is determined within the electronic assembly based on the updated model, wherein the resulting force equilibrium is determined by removing the displacement from the updated model.
Data characterizing an assembly/structure containing a first stage and a second stage are received in a computer system. The first stage can contain a first cyclic symmetry and the second stage can contain a second cyclic symmetry. The first cyclic symmetry and the second cyclic symmetry are different from each other. Each stage may be a 360-degree stage. The received data includes a first mesh representing the first stage and a second mesh representing the second stage. Multiple simulation results are obtained using corresponding models in respective dynamic analyses of the assembly. Each model includes a set of constraints for coupling the first mesh and the second mesh. The set of constraints is associated with a group of distinct linked harmonic indices. Physical behaviors of the assembly are then calculated using one or more sets of the simulation results. Constraints are in forms of constraint equations.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 119/22 - Yield analysis or yield optimisation
G06F 30/398 - Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM]
90.
SYSTEM AND METHOD OF DETERMINING RESPIRATORY PARTICLE DEPOSITION IN A LUNG
A method and a system of determining respiratory particle deposition in a lung is described. A single conduit model having conduit generations corresponding to generations of the lung is generated. Computational fluid dynamics simulations of respiratory particle delivery to an N conduit generation and an N+1 conduit generation of the single conduit model are performed to determine a mass of respiratory particles leaving the N conduit generation and a mass of respiratory particles deposited in the N+1 conduit generation. The mass deposited in the N+1 conduit generation is scaled up by a scaling factor, the scaling factor being based on the mass of respiratory particles leaving the N conduit generation, to determine a total mass of respiratory particles deposited in a generation of the lung corresponding to the N+1 conduit generation. Other embodiments are also described and claimed.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
91.
Systems and methods for simulating operation of a battery pack
A system for simulating operation of a battery pack comprising a plurality of battery modules includes an electrical model configured to simulate electrical behavior of a respective battery module and a thermal model configured to simulate thermal behavior of the respective battery module. The electrical model provides outputs coupled as inputs to the thermal model, and the thermal model provides outputs coupled as inputs to the electrical model. A coolant model is configured to couple with the thermal model, the coolant model to simulate cooling of the respective battery module based on a temperature and a heat transfer coefficient associated with the respective battery module.
A method and apparatus of a device for obtaining a geometric representation of an object including a thin structure having a first surface and a second surface; generating one or more 2D blocking faces as a simplified representation of one of the first surface or the second surface; generating one or more 3D blocks based on an extrusion of the one or more 2D blocking faces; and determining a 3D mesh of the object based on the one or more 3D blocks.
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
Extraction of capacitance values from a design of an electrical circuit can use a set of trained neural networks to generate extracted capacitance values from the circuit using a representation of the Green's function. A method can include the following operations: storing a machine learning model that includes a trained set of one or more neural networks that have been trained to calculate a representation of a Green's function to extract capacitance values from a design of a circuit having a set of conductors; applying, to the machine learning model, a set of inputs representing the set of conductors and their surrounding dielectric materials; encoding the set of inputs through a trained encoder to generate a latent space representation; calculating the values of the Green's function from the latent space representation through a dedicated trained neural network; and calculating the values of the gradient of the Green's function from the latent space representation through another dedicated trained neural network.
A global computer aided engineering (CAE) model representing an electronic product that contains solder joints and an individual detailed solder joint model are received. The solder joint model can include a solder ball, one or more metal pads, a portion of printed circuit board, and a portion of semiconductor chip component. The global CAE model includes locations of the solder joints to be evaluated in a drop test simulation. The solder joint model is replicated at each location to create a local CAE model via a geometric relationship between the global CAE model and the local CAE model. Simulated physical behaviors of the product under a design condition are obtained in a co-simulation using the global CAE model in a first time scale and the local CAE model in a second time scale. Simulated physical behaviors are periodically synchronized based on kinematic and force constraints.
G06F 30/20 - Design optimisation, verification or simulation
G06F 119/02 - Reliability analysis or reliability optimisationFailure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
G06F 30/25 - Design optimisation, verification or simulation using particle-based methods
G06F 119/22 - Yield analysis or yield optimisation
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
Analysis of power supply noise in simulations of a design of a circuit can use per instance dynamic voltage drops (DVD) in timing analyses so that the simulated DVD values on a per victim cell basis can accurately guide the timing analysis on each victim instead of a global DVD for all victims during the timing analysis. In one embodiment, a method can: determine, during a power analysis simulation, a representation of an energy lost, during each switching window at each output of each victim cell, at one or more power supply rails of each of the victim cells in the set of victim cells due to aggressors in the design; and provide the representation of the energy lost separately for each victim cell to a timing analysis system. The representation can be a rectangle having a width defined by a switching window of a victim's output.
Example systems and methods are disclosed for predicting structural distortion in a part geometry created by an additive manufacturing process. An octree mesh is generated for a part geometry based on a voxel file having voxel layers. The octree mesh is coarsened, a representation of stiffness for one of the voxel layers is generated based on the coarsened octree mesh, a force vector is determined based, in part, on the coarsened octree mesh and data from a thermal analysis of the part geometry, and a layer distortion is determined based, in part, on the force vector and the representation of stiffness. The structural distortion in the part geometry is predicted based on the layer distortion.
Methods, systems and media for simulating or analyzing voltage drops in a power distribution network can use an incremental approach to define a portion of a design around a victim to capture a sufficient collection of aggressors that cause appreciable voltage drop on the victim, and then an incremental simulation of just the portion can be performed rather than computing simulated voltage drops across the entire design. This approach can be both computationally efficient and can limit the size of the data used in simulating dynamic voltage drops in the power distribution network. Multiple different portions can be simulated separately in separate processing cores or elements. In one embodiment, a system can provide options of user selected constraints for the simulation to provide better accuracy or use less memory. Better accuracy will normally use a larger set of aggressors for each victim at the expense of using more memory.
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
G06F 119/06 - Power analysis or power optimisation
98.
METHOD AND SYSTEM FOR REAL-TIME SIMULATIONS USING CONVERGENCE STOPPING CRITERION
System and method is disclosed for a real-time simulation of a physical system using an iterative solver, such as a finite element solver, or other solvers having a mesh size independent stopping criterion. The method includes: calculating a potential solution for a set of equations used to simulate a characteristic for a model modeling a physical system; determining a difference between the potential solution and a previously calculated potential solution; determining if a stop condition is satisfied based on the difference, where the calculation is performed iteratively until the stop condition is satisfied; and generating an output to indicate the potential solution as a steady state solution or a stall solution for the simulated characteristic of the physical system according to the determining if the stop condition is satisfied.
A first computer-aided design (CAD) model to represent a physical object is received that includes a triangle mesh with boundary condition faces. Thereafter, the triangle mesh is smoothed such that the boundary condition faces are maintained. The smoothed triangle mesh is then segmented. As part of such segmenting, a plurality quads are generated. Each of the quads is then fitted with a non-uniform rational basis spline (nurbs) patch. A second CAD model is next generated to represent the physical object which is based on the plurality of quads fitted with nurbs. Related apparatus, systems, techniques and articles are also described.
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
A method is disclosed for adapting the shooting and bouncing rays (SBR) model used in EM field simulation to incorporate incoherent effects of rough surfaces. It introduces a data-driven framework that utilizes on an ensemble of bistatic polarized RCS measurements to characterize the aggregate coherent and incoherent scattering contributions from microscopic surface roughness by compiling field statistics into bidirectional scattering distribution functions (BSDFs). It generalizes an analytical model for rough surface microwave scattering with no assumptions made on surface statistics. The method includes a technique for computing properly correlated fluctuations in the incoherent field across frequency and incident/observation angle. The technique exploits the scale invariance of the incoherent fluctuations to adapt these fluctuations to ray footprints with arbitrary size with the size of the sample surface. The sampling method accurately reproduces the statistical distribution of scattered fields observed from direct simulation of rough surfaces where microscopic roughness is explicitly modeled.
