Kazemi et al., 2022 - Google Patents
Multiphysics design optimization via generative adversarial networksKazemi et al., 2022
View HTML- Document ID
- 16614404856478379130
- Author
- Kazemi H
- Seepersad C
- Alicia Kim H
- Publication year
- Publication venue
- Journal of Mechanical Design
External Links
Snippet
This work presents a method for generating concept designs for coupled multiphysics problems by employing generative adversarial networks (GANs). Since the optimal designs of multiphysics problems often contain a combination of features that can be found in the …
- 238000005457 optimization 0 title abstract description 59
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5086—Mechanical design, e.g. parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
- G06F2217/16—Numerical modeling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Nie et al. | Topologygan: Topology optimization using generative adversarial networks based on physical fields over the initial domain | |
Oh et al. | Deep generative design: Integration of topology optimization and generative models | |
Chen et al. | Padgan: Learning to generate high-quality novel designs | |
Deng et al. | A parametric level set method for topology optimization based on deep neural network | |
Ates et al. | Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization | |
Chen et al. | Synthesizing designs with interpart dependencies using hierarchical generative adversarial networks | |
Tai et al. | Design synthesis of path generating compliant mechanisms by evolutionary optimization of topology and shape | |
Tai et al. | Design of structures and compliant mechanisms by evolutionary optimization of morphological representations of topology | |
Behzadi et al. | Gantl: Toward practical and real-time topology optimization with conditional generative adversarial networks and transfer learning | |
Natekar et al. | Constructive solid analysis: a hierarchical, geometry-based meshless analysis procedure for integrated design and analysis | |
Shahan et al. | Bayesian network classifiers for set-based collaborative design | |
Whalen et al. | Toward reusable surrogate models: Graph-based transfer learning on trusses | |
Kazemi et al. | Multiphysics design optimization via generative adversarial networks | |
Napier et al. | An artificial neural network approach for generating high-resolution designs from low-resolution input in topology optimization | |
Li et al. | Using physics-informed generative adversarial networks to perform super-resolution for multiphase fluid simulations | |
Bielecki et al. | Multi-stage deep neural network accelerated topology optimization | |
Rastegarzadeh et al. | Neural network-assisted design: a study of multiscale topology optimization with smoothly graded cellular structures | |
Li et al. | Graphfit: Learning multi-scale graph-convolutional representation for point cloud normal estimation | |
Nobari et al. | Range-constrained generative adversarial network: Design synthesis under constraints using conditional generative adversarial networks | |
Seo et al. | Development of deep convolutional neural network for structural topology optimization | |
Liu et al. | Multifidelity physics-constrained neural networks with minimax architecture | |
Rostami et al. | Cooperative coevolutionary topology optimization using moving morphable components | |
Xing et al. | Shared-gaussian process: Learning interpretable shared hidden structure across data spaces for design space analysis and exploration | |
Parrott et al. | Multidisciplinary topology optimization using generative adversarial networks for physics-based design enhancement | |
Chen et al. | Gan-duf: Hierarchical deep generative models for design under free-form geometric uncertainty |