WO2020056107A1 - Pipeline de simulation automatisée pour conception assistée par ordinateur générée par simulation rapide - Google Patents

Pipeline de simulation automatisée pour conception assistée par ordinateur générée par simulation rapide Download PDF

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Publication number
WO2020056107A1
WO2020056107A1 PCT/US2019/050773 US2019050773W WO2020056107A1 WO 2020056107 A1 WO2020056107 A1 WO 2020056107A1 US 2019050773 W US2019050773 W US 2019050773W WO 2020056107 A1 WO2020056107 A1 WO 2020056107A1
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Prior art keywords
design
simulation
boundary conditions
module
known designs
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PCT/US2019/050773
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English (en)
Inventor
Lucia MIRABELLA
Livio Dalloro
Sanjeev SRIVASTAVA
Tsz Ling Elaine TANG
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Siemens Aktiengesellschaft
Siemens Corporation
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Publication of WO2020056107A1 publication Critical patent/WO2020056107A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Definitions

  • This application relates to simulations for computer aided design of three dimensional (3D) objects. More particularly, this application relates to automated simulation-driven models of 3D objects.
  • the process of designing 3D objects often involves the use of simulation tools to evaluate the performance of the physical object before the design is finalized and/or manufactured. For example, simulation can predict whether the designed object possesses physical characteristics that can withstand particular stresses or other criteria upon fabrication. While 3D simulation is advantageous, it is a costly process in time consumed for computational processing involved. For example, a complex computer aided design (CAD) simulation can take several days to execute, which can be a significant loss, particularly if the simulation fails and must be repeated. Moreover, simulations may involve multiple interacting physics and may be human-dependent (e.g., required steps that are not automated). Consequently, the available number of practical designs that can be evaluated are very limited, including both stand-alone evaluations and design optimizations.
  • CAD computer aided design
  • Typical solutions include using human expertise to perform some of the required steps, limiting the number of design points to evaluate, or using simplified simulation models that allow for faster evaluations at the price of lower accuracy. Hence, it is desirable for a CAD system with automated capability of exploring designs without such limitations.
  • a computer aided design system for simulation driven design of a three-dimensional (3D) object includes a memory having a plurality of application modules stored thereon, and a processor for executing the application modules.
  • a boundary condition extraction module extracts from a knowledge base a set of boundary conditions for each of a plurality of various known designs of 3D objects related to a new design for a proposed 3D object by inferring a translation of typical usage of the known designs to the set of boundary conditions required for evaluating performance of a simulation of the new design.
  • a design exploration module automatically generates a plurality of design candidates for the new design according to a data-driven generative design algorithm, each design candidate configured as a 3D geometric representation of the proposed 3D object.
  • a morphing module transforms design independent boundary conditions into design-specific boundary conditions for each of the design candidates.
  • a performance prediction module is configured to generate a set of key performance indicators (e.g., as either single aggregated values or 3D maps) as a prediction of a design simulation performance.
  • the performance prediction module may include a plurality of machine learning-based models for decomposing the performance prediction of the new design for more efficient processing of a complex multi-parameter analysis.
  • a design selection module explores the decomposed performance predictions of the design candidates and determine a ranking of design candidates according to key performance indicators of the design candidates.
  • FIG. 1 shows an example of a computer aided design system according to embodiments of this disclosure
  • FIG. 2 shows a flowchart example of an automated pipeline for fast simulation driven computer aided design according to embodiments of this disclosure
  • FIG. 3 shows a block diagram example of decomposed training for a machine learning- based model according to embodiments of this disclosure
  • FIG. 4 shows an example of a computing environment within which embodiments of this disclosure may be implemented.
  • FIG. 1 shows an example of a computer aided design system according to embodiments of this disclosure.
  • a computer aided design (CAD) system 100 includes a processor 120 used to execute program modules stored in memory 110, which include a boundary condition extractor 111 , a design exploration module 112, a morphing module 113, performance prediction engine 114, and design selection module 115.
  • CAD computer aided design
  • FIG. 2 shows a flowchart example of an automated pipeline for fast simulation driven computer aided design according to embodiments of this disclosure.
  • Pipeline 200 will be described with reference to FIG. 1.
  • boundary condition extractor 111 develops design independent boundary conditions (BC) for known designs related to a new design of a proposed project.
  • Knowledge base 211 may store various forms of data related to known designs of 3D objects, such as video data showing the object in use, reacting to the physical environment and to the user.
  • Other forms of data related to known designs stored in knowledge base 211 may include documents associated with the known designs, material and shape information, and sensor measurement data.
  • boundary condition extractor 1 1 1 is configured to extract, from a knowledge base 21 1 , a set of boundary conditions for each of a plurality of various known designs of 3D objects related to a new design for a proposed 3D object by inferring a translation of typical usage of the known designs to the set of boundary conditions required for evaluating performance of a simulation of the new design.
  • the inference for translation may be based on processing of video data from the knowledge base by observing physical displacement from stresses imposed on each of the 3D objects of the known designs in the context of typical usage. The advantage of factoring usage into the evaluation is to support a more robust simulation evaluation for the new design, further down the pipeline 200, based on dynamic parameters learned from known designs.
  • the extractor 1 1 1 may measure reflex or deformations of the 3D objects as observed in action videos to identify boundary conditions, such as stress points useful for later simulation of a new design. As such, the extractor 1 1 1 infers boundary conditions from observed typical usage of known designs. Action videos in knowledge base 21 1 may also be informative of typical physical boundaries that would impede use of the known object (e.g., such as common obstacles in the vicinity) and the boundary condition extractor 1 1 1 may determine whether physical boundaries have an influence on the known design to be included as a boundary condition. According to other aspects, the inference may be based on natural language processing of documents in the knowledge base associated with the known designs.
  • the inference may be based on sensor measurements of known designs extracted from the knowledge base.
  • sensor measurements may provide measurements of environment characteristics related to typical usage which may be translated to how an object typically reacts or behaves to temperature, humidity or other environmental factors, which can then be translated to boundary conditions according to extreme values, mean values, or the like.
  • the boundary condition extractor 1 1 1 generates a set of complete boundary conditions for each existing design. From the extracted boundary conditions, a set of design-independent boundary conditions is determined by the extractor 1 1 1 by one or more algorithms, such as a flood fill algorithm, or mapping between a local boundary shape and a boundary condition value from a set of training examples.
  • stage 202 alternative designs are generated by design exploration module 1 12.
  • new design candidates may be automatically generated by exploring new topologies according to a topology optimization algorithm, or performing parametric variation of existing parametrized designs.
  • a data-driven generative design algorithm may be applied by design exploration module 1 12.
  • the output of stage 202 may be a 3D shape representing the object for performance evaluation.
  • An additional source of information to drive the alternative designs includes performance prediction results of stage 204, including feedback that can be interpreted by design exploration module 1 12 to determine the optimality of a new design candidate and to drive the generation of next design. For example, the feedback can be used to determine whether a new design candidate satisfies functional requirements (e.g., thermal flux, physical stress, or others).
  • functional requirements e.g., thermal flux, physical stress, or others.
  • morphing module 1 13 may transform design independent boundary conditions generated at stage 201 into design-specific boundary conditions associated with each respective new design candidate produced by stage 202.
  • a flood-fill algorithm may be executed to morph the design independent boundary conditions to boundary conditions specific to a new design candidate.
  • a representation of predicted design simulation performance is computed by performance prediction module 1 14 as a set of KPIs (e.g., as either single aggregated values or 3D maps).
  • the input to this step is the 3D shape of the design candidate and the set of boundary conditions for the desired physics.
  • 3D physics-based simulation tools can be used to solve the partial differential equations that describe the physics of interest. Examples are tools based on finite element method, finite difference method, or the like. These high-fidelity simulation approaches can be made faster using parallel computing techniques on high performance computing clusters or using GPU-based programming. Results of these simulations are added to stored prior simulation results 242.
  • performance prediction module 1 14 may be configured as a machine-learning based model for performance prediction of the new design.
  • the spectrum of design parameters is typically very large (e.g., involving very different shapes, very different boundary conditions and also different discretization of the shape (i.e. , mesh)).
  • it is a complex task to be able to predict the performance of a new design (e.g., design point and desired usage) from past simulations.
  • the performance prediction complexity may be decomposed during model learning according to division of training with unique sets of parameters, including one or more fixed parameters and one or more variable parameters.
  • stage 204 arranges a correspondence between a model with one or more fixed parameters (e.g., geometry) to results of prior simulations having commonly shared parameters that are the same as the fixed parameters (e.g., geometry).
  • KPIs 306 are fed back to stage 202 as input for driving alternative designs, using prediction performance results to direct alternative designs (e.g., by convergence onto a design point).
  • Stage 204 performance prediction may include a combination of the above described options. For example, if there are previous designs that share similarities with the new design, the machine learning based model can be leveraged over a high fidelity 3D simulation to take advantage of obtaining prediction results faster. On the other hand, if the new design is very different from the previous design that has prior simulation results, then a high-fidelity simulation is a preferable option.
  • FIG. 3 shows a block diagram example of decomposed training for a machine learning-based model according to embodiments of this disclosure.
  • the following non- limiting example is described for a design simulation involving three parameters (boundary condition set, geometry, shape discretization), which influence the division of training phases for the machine learning-based model. Variations may include more or less parameters for evaluation, where there is a one-to-one correspondence for a parameter to a respective training phase.
  • Training data consists of simulation results 242 of known designs, and graphs 241 of multiple meshes and fields of known designs of an object being designed. Graphs 241 are generated from prior simulation results 242 and represent relationship between previous design features and parameters and prior simulation results 242. The graphs 241 are queried to explore such relationship in order to extract relevant knowledge for new design candidates.
  • a parameter set is defined performance prediction module 1 14.
  • a defined parameter set for simulation of a new design candidate may include a particular geometry, a boundary condition set, and a shape discretization.
  • a machine learning-based model may be configured to include a neural network (e.g., DNN, ANN, CNN, RNN or residual neural network (ResNet)), which may be trained for performance simulation of a candidate design.
  • a neural network e.g., DNN, ANN, CNN, RNN or residual neural network (ResNet)
  • ResNet residual neural network
  • Several machine learning-based training phases may be configured to decompose a multiparameter simulation. Each performance prediction training phase may approximate performance for a simulation (e.g., predicting key performance indicators (KPIs)) on unique combinations of fixed parameter(s) and variable parameter(s).
  • KPIs key performance indicators
  • a first training phase may train the model 314 to predict KPIs for a design candidate based on simulation results of a first parameter grouping 302.
  • training data for the first training phase may be a 3D object shape of a known design 301 , where during training, training settings 304a define layers of the model 314 to keep weights and bias fixed for geometry and boundary conditions nodes according to fixed parameter selection 305, while varying weights and bias of node activation for a shape discretization (mesh) parameter.
  • Fixed parameter selection 305 aligns the correspondence of the first grouping of simulation results, which have the same geometry and boundary conditions, to the fixed parameter of training settings 304a.
  • meshes are varied to check how the solution is sensitive to the current KPI. If KPI results are independent of the mesh variations, there is an indication that the results are reliable.
  • the machine learning-based model 314 may be trained to learn prediction of performance based on fixing geometry and shape discretization weights and bias of the model while varying boundary conditions.
  • varied boundary conditions may relate to operation states, such as runtime, start-up, and shutdown to generate different KPI values (i.e. , different performance prediction) for the respective states.
  • fixed parameter selection 305 aligns grouping of simulation results 302, which are prior simulations of known designs 301 all with the same geometry and shape discretization, with fixed weights and bias for the same parameters in training settings 304b.
  • the model 314 may learn to predict performance based on a third grouping of simulation results 302 for known designs 301 , using training settings 304c with fixed boundary condition weights and bias of the model while varying geometry and shape discretization weights and bias.
  • Such a parameter selection allows prediction of how a given design (geometry) will perform under a given condition (boundary condition). Since the shape discretization can also change with the geometry, this training phase includes varying both geometry and shape discretization.
  • operation of pipeline 200 is enabled to execute automatic prediction of design candidate performance without high fidelity simulation of the design, for any design candidate of any combination of parameters associated with the training (e.g., such as geometry, boundary condition set and shape discretization).
  • the input to performance prediction stage 204 is a design candidate 301 in the form of its 3D geometric representation from stage 202, and design specific boundary conditions 302 determined by morphing stage 203.
  • the prediction model automatically predicts a set of KPIs 306 for the design candidate based on the learning from prior simulation results 242 of known designs.
  • a 3D mapping of the KPI quantity values or vectors can be generated by stage 204, which can be rendered as lumped KPIs within a 3D geometry for the design structure.
  • a 3D mesh of nodes is rendered to represent the geometry of the design structure, and KPIs 306 are mapped to one or more nodes of the mesh.
  • Computed quantity values or lumped versions of the values (e.g., an average value of the quantities for a structural region or specific location) that are outputs of prior simulations can be mapped to the 3D geometry of the design structure.
  • the prediction model may generate velocity vectors as the set of KPIs 306 to represent the fluid flow through the pipe structure.
  • the set of velocity vectors can be mapped to mesh nodes for a prediction of performance of the pipe structure with respect to fluid flow velocity.
  • the set of KPIs 306 may be temperature values mapped to the 3D geometry to predict thermal stress performance of a design structure. As new mappings are generated, the results may be added to stored graphs 241 .
  • the performance prediction stage 204 generates KPIs 306 of a type that is relevant to performance of the design structure, based on learning from prior simulations 242.
  • KPIs 306 for all design candidates are compared and a best design is selected according to a ranking algorithm that applies selection criteria, which may be predetermined by a user.
  • FIG. 4 illustrates an example of a computing environment within which embodiments of the present disclosure may be implemented.
  • a computing environment 400 includes a computer system 410 that may include a communication mechanism such as a system bus 421 or other communication mechanism for communicating information within the computer system 410.
  • the computer system 410 further includes one or more processors 420 coupled with the system bus 421 for processing the information.
  • computing environment 400 corresponds to a CAD system, in which the computer system 410 relates to a computer described below in greater detail.
  • the processors 420 may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art. More generally, a processor as described herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processor may also comprise memory storing machine-readable instructions executable for performing tasks. A processor acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and/or by routing the information to an output device.
  • CPUs central processing units
  • GPUs graphical processing units
  • a processor may use or comprise the capabilities of a computer, controller or microprocessor, for example, and be conditioned using executable instructions to perform special purpose functions not performed by a general purpose computer.
  • a processor may include any type of suitable processing unit including, but not limited to, a central processing unit, a microprocessor, a Reduced Instruction Set Computer (RISC) microprocessor, a Complex Instruction Set Computer (CISC) microprocessor, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a System-on-a-Chip (SoC), a digital signal processor (DSP), and so forth.
  • RISC Reduced Instruction Set Computer
  • CISC Complex Instruction Set Computer
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • SoC System-on-a-Chip
  • DSP digital signal processor
  • processor(s) 420 may have any suitable microarchitecture design that includes any number of constituent components such as, for example, registers, multiplexers, arithmetic logic units, cache controllers for controlling read/write operations to cache memory, branch predictors, or the like.
  • the microarchitecture design of the processor may be capable of supporting any of a variety of instruction sets.
  • a processor may be coupled (electrically and/or as comprising executable components) with any other processor enabling interaction and/or communication there-between.
  • a user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof.
  • a user interface comprises one or more display images enabling user interaction with a processor or other device.
  • the system bus 421 may include at least one of a system bus, a memory bus, an address bus, or a message bus, and may permit exchange of information (e.g., data (including computer-executable code), signaling, etc.) between various components of the computer system 410.
  • the system bus 421 may include, without limitation, a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and so forth.
  • the system bus 421 may be associated with any suitable bus architecture including, without limitation, an Industry Standard Architecture (ISA), a Micro Channel Architecture (MCA), an Enhanced ISA (EISA), a Video Electronics Standards Association (VESA) architecture, an Accelerated Graphics Port (AGP) architecture, a Peripheral Component Interconnects (PCI) architecture, a PCI-Express architecture, a Personal Computer Memory Card International Association (PCMCIA) architecture, a Universal Serial Bus (USB) architecture, and so forth.
  • ISA Industry Standard Architecture
  • MCA Micro Channel Architecture
  • EISA Enhanced ISA
  • VESA Video Electronics Standards Association
  • AGP Accelerated Graphics Port
  • PCI Peripheral Component Interconnects
  • PCMCIA Personal Computer Memory Card International Association
  • USB Universal Serial Bus
  • the computer system 410 may also include a system memory 430 coupled to the system bus 421 for storing information and instructions to be executed by processors 420.
  • the system memory 430 may include computer readable storage media in the form of volatile and/or nonvolatile memory, such as read only memory (ROM) 431 and/or random access memory (RAM) 432.
  • the RAM 432 may include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM).
  • the ROM 431 may include other static storage device(s) (e.g., programmable ROM, erasable PROM, and electrically erasable PROM).
  • system memory 430 may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors 420.
  • a basic input/output system 433 (BIOS) containing the basic routines that help to transfer information between elements within computer system 410, such as during start-up, may be stored in the ROM 431.
  • RAM 432 may contain data and/or program modules that are immediately accessible to and/or presently being operated on by the processors 420.
  • System memory 430 may additionally include, for example, operating system 434, application modules 435, and other program modules 436.
  • Application modules 435 may include aforementioned modules described for FIG. 1 and may also include a user portal for development of the application program, allowing input parameters to be entered and modified as necessary.
  • the operating system 434 may be loaded into the memory 430 and may provide an interface between other application software executing on the computer system 410 and hardware resources of the computer system 410. More specifically, the operating system 434 may include a set of computer-executable instructions for managing hardware resources of the computer system 410 and for providing common services to other application programs (e.g., managing memory allocation among various application programs). In certain example embodiments, the operating system 434 may control execution of one or more of the program modules depicted as being stored in the data storage 440.
  • the operating system 434 may include any operating system now known or which may be developed in the future including, but not limited to, any server operating system, any mainframe operating system, or any other proprietary or non- proprietary operating system.
  • the computer system 410 may also include a disk/media controller 443 coupled to the system bus 421 to control one or more storage devices for storing information and instructions, such as a magnetic hard disk 441 and/or a removable media drive 442 (e.g., floppy disk drive, compact disc drive, tape drive, flash drive, and/or solid state drive).
  • Storage devices 440 may be added to the computer system 410 using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire).
  • Storage devices 441 , 442 may be external to the computer system 410.
  • the computer system 410 may include a user input interface or graphical user interface (GUI) 461 , which may comprise one or more input devices, such as a keyboard, touchscreen, tablet and/or a pointing device, for interacting with a computer user and providing information to the processors 420.
  • GUI graphical user interface
  • the computer system 410 may perform a portion or all of the processing steps of embodiments of the invention in response to the processors 420 executing one or more sequences of one or more instructions contained in a memory, such as the system memory 430. Such instructions may be read into the system memory 430 from another computer readable medium of storage 440, such as the magnetic hard disk 441 or the removable media drive 442.
  • the magnetic hard disk 441 and/or removable media drive 442 may contain one or more data stores and data files used by embodiments of the present disclosure.
  • the data store 440 may include, but are not limited to, databases (e.g., relational, object-oriented, etc.), file systems, flat files, distributed data stores in which data is stored on more than one node of a computer network, peer-to-peer network data stores, or the like. Data store contents and data files may be encrypted to improve security.
  • the processors 420 may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory 430.
  • hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.
  • the computer system 410 may include at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein.
  • the term“computer readable medium” as used herein refers to any medium that participates in providing instructions to the processors 420 for execution.
  • a computer readable medium may take many forms including, but not limited to, non- transitory, non-volatile media, volatile media, and transmission media.
  • Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks, such as magnetic hard disk 441 or removable media drive 442.
  • Non-limiting examples of volatile media include dynamic memory, such as system memory 430.
  • Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up the system bus 421 .
  • Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
  • Computer readable medium instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • electronic circuitry including, for example, programmable logic circuitry, field- programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
  • the computing environment 400 may further include the computer system 410 operating in a networked environment using logical connections to one or more remote computers, such as remote computing device 473 and remote agents 481.
  • the network interface 470 may enable communication, for example, with other remote devices 473 or systems and/or the storage devices 441 , 442 via the network 471 .
  • Remote computing device 473 may be a person5al computer (laptop or desktop), a mobile device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system 410.
  • computer system 410 may include modem 472 for establishing communications over a network 471 , such as the Internet. Modem 472 may be connected to system bus 421 via user network interface 470, or via another appropriate mechanism.
  • Network 471 may be any network or system generally known in the art, including the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between computer system 410 and other computers (e.g., remote computing device 473).
  • the network 471 may be wired, wireless or a combination thereof. Wired connections may be implemented using Ethernet, Universal Serial Bus (USB), RJ-6, or any other wired connection generally known in the art.
  • Wireless connections may be implemented using Wi-Fi, WiMAX, and Bluetooth, infrared, cellular networks, satellite or any other wireless connection methodology generally known in the art. Additionally, several networks may work alone or in communication with each other to facilitate communication in the network 471 .
  • program modules, applications, computer- executable instructions, code, or the like depicted in FIG. 4 as being stored in the system memory 430 are merely illustrative and not exhaustive and that processing described as being supported by any particular module may alternatively be distributed across multiple modules or performed by a different module.
  • various program module(s), script(s), plug-in(s), Application Programming Interface(s) (API(s)), or any other suitable computer-executable code hosted locally on the computer system 410, the remote device 473, and/or hosted on other computing device(s) accessible via one or more of the network(s) 471 may be provided to support functionality provided by the program modules, applications, or computer-executable code depicted in FIG.
  • functionality may be modularized differently such that processing described as being supported collectively by the collection of program modules depicted in FIG. 4 may be performed by a fewer or greater number of modules, or functionality described as being supported by any particular module may be supported, at least in part, by another module.
  • program modules that support the functionality described herein may form part of one or more applications executable across any number of systems or devices in accordance with any suitable computing model such as, for example, a client-server model, a peer-to-peer model, and so forth.
  • any of the functionality described as being supported by any of the program modules depicted in FIG. 4 may be implemented, at least partially, in hardware and/or firmware across any number of devices.
  • the computer system 410 may include alternate and/or additional hardware, software, or firmware components beyond those described or depicted without departing from the scope of the disclosure. More particularly, it should be appreciated that software, firmware, or hardware components depicted as forming part of the computer system 410 are merely illustrative and that some components may not be present or additional components may be provided in various embodiments. While various illustrative program modules have been depicted and described as software modules stored in system memory 430, it should be appreciated that functionality described as being supported by the program modules may be enabled by any combination of hardware, software, and/or firmware. It should further be appreciated that each of the above-mentioned modules may, in various embodiments, represent a logical partitioning of supported functionality.
  • This logical partitioning is depicted for ease of explanation of the functionality and may not be representative of the structure of software, hardware, and/or firmware for implementing the functionality. Accordingly, it should be appreciated that functionality described as being provided by a particular module may, in various embodiments, be provided at least in part by one or more other modules. Further, one or more depicted modules may not be present in certain embodiments, while in other embodiments, additional modules not depicted may be present and may support at least a portion of the described functionality and/or additional functionality. Moreover, while certain modules may be depicted and described as sub- modules of another module, in certain embodiments, such modules may be provided as independent modules or as sub-modules of other modules.
  • any operation, element, component, data, or the like described herein as being based on another operation, element, component, data, or the like can be additionally based on one or more other operations, elements, components, data, or the like. Accordingly, the phrase“based on,” or variants thereof, should be interpreted as “based at least in part on.”
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block may occur out of the order noted in the Figures.
  • two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

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Abstract

La présente invention concerne un système de conception assistée par ordinateur pour la conception générée par simulation d'un modèle tridimensionnel (3D) comprenant un module d'extraction de conditions limites qui extrait un ensemble de conditions limites pour chaque conception parmi une pluralité de différentes conceptions connues d'objets 3D associées à une nouvelle conception pour un objet 3D proposé et qui génère un ensemble de conditions limites indépendantes de conception représentatives d'une utilisation typique des conceptions connues. Le module d'exploration de conception génère une pluralité de candidats de conception pour la nouvelle conception. Le module de transformation transforme les conditions limites indépendantes de la conception en conditions limites spécifiques à la conception pour chaque candidat de conception. Le module de prédiction de performance comprend un modèle de réseau neuronal formé pour prédire les performances de chaque candidat de conception sur la base d'un apprentissage à partir de résultats de simulation antérieurs de conceptions connues, et il génère un ensemble d'indicateurs de performance clés pour chaque candidat de conception. Le meilleur choix de conception est sélectionné parmi les candidats de conception sur la base des indicateurs de performance clés.
PCT/US2019/050773 2018-09-14 2019-09-12 Pipeline de simulation automatisée pour conception assistée par ordinateur générée par simulation rapide WO2020056107A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113723601A (zh) * 2021-08-30 2021-11-30 北京市商汤科技开发有限公司 神经网络模型的转换方法、装置、设备及存储介质

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7751917B2 (en) * 2002-04-26 2010-07-06 Bae Systems Plc Optimisation of the design of a component

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7751917B2 (en) * 2002-04-26 2010-07-06 Bae Systems Plc Optimisation of the design of a component

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
P KESTEL ET AL: "FEATURE-BASED APPROACH FOR THE AUTOMATED SETUP OF ACCURATE, DESIGN ACCOMPANYING FINITE ELEMENT ANALYSES", INTERNATIONAL DESIGN CONFERENCE - DESIGN 2016, 19 May 2016 (2016-05-19), pages 697 - 706, XP055653550 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113723601A (zh) * 2021-08-30 2021-11-30 北京市商汤科技开发有限公司 神经网络模型的转换方法、装置、设备及存储介质

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