Zeng et al., 2021 - Google Patents
Accelerated basis adaptation in homogeneous chaos spacesZeng et al., 2021
View PDF- Document ID
- 3851328641147993888
- Author
- Zeng X
- Red-Horse J
- Ghanem R
- Publication year
- Publication venue
- Computer Methods in Applied Mechanics and Engineering
External Links
Snippet
Polynomial chaos expansions (PCE) provide an efficient approach to uncertainty quantification (UQ) and have been adapted to diverse applications across the spectrum of science and engineering. For situations involving large stochastic parameterizations, the …
- 230000004301 light adaptation 0 title abstract description 196
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
-
- 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
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- 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
- 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
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
-
- 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
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- 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
- 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
- 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 |
---|---|---|
Wang et al. | On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks | |
Geneva et al. | Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks | |
Zhu et al. | Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data | |
Olivier et al. | UQpy: A general purpose Python package and development environment for uncertainty quantification | |
Zeng et al. | Accelerated basis adaptation in homogeneous chaos spaces | |
Vu-Bac et al. | A software framework for probabilistic sensitivity analysis for computationally expensive models | |
Audouze et al. | Reduced‐order modeling of parameterized PDEs using time–space‐parameter principal component analysis | |
Frangos et al. | Surrogate and reduced‐order modeling: a comparison of approaches for large‐scale statistical inverse problems | |
Xu | A new method for reliability assessment of structural dynamic systems with random parameters | |
Lim et al. | Distribution-free polynomial chaos expansion surrogate models for efficient structural reliability analysis | |
Soize et al. | Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics | |
Fan et al. | Locally optimal reach set over-approximation for nonlinear systems | |
Boelens et al. | Parallel tensor methods for high-dimensional linear PDEs | |
Bonnin | Amplitude and phase dynamics of noisy oscillators | |
Liao et al. | An adaptive reduced basis ANOVA method for high-dimensional Bayesian inverse problems | |
Matsuda et al. | Estimation of ordinary differential equation models with discretization error quantification | |
Chen et al. | Gpt-pinn: Generative pre-trained physics-informed neural networks toward non-intrusive meta-learning of parametric pdes | |
Jiang et al. | Multiscale model reduction method for Bayesian inverse problems of subsurface flow | |
Tsilifis et al. | Sparse Polynomial Chaos expansions using variational relevance vector machines | |
Tabandeh et al. | Numerical solution of the Fokker–Planck equation using physics-based mixture models | |
Cheng et al. | Active learning polynomial chaos expansion for reliability analysis by maximizing expected indicator function prediction error | |
Zeng et al. | Projection pursuit adaptation on polynomial chaos expansions | |
Kim et al. | Adaptive active subspace-based metamodeling for high-dimensional reliability analysis | |
Giacoma et al. | An efficient quasi-optimal space-time PGD application to frictional contact mechanics | |
Shen et al. | Self-consistency of the fokker planck equation |