Franco et al., 2024 - Google Patents
Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reductionFranco et al., 2024
View PDF- Document ID
- 17867080089042026773
- Author
- Franco N
- Manzoni A
- Zunino P
- Hesthaven J
- Publication year
- Publication venue
- arXiv preprint arXiv:2404.18841
External Links
Snippet
We develop a novel deep learning technique, termed Deep Orthogonal Decomposition (DOD), for dimensionality reduction and reduced order modeling of parameter dependent partial differential equations. The approach consists in the construction of a deep neural …
- 238000013459 approach 0 title abstract description 64
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
- G06F17/10—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
- G06F17/50—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
- G06F17/16—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
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- 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
- 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
-
- 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
- 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 |
---|---|---|
Mo et al. | Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media | |
Chen et al. | Physics-informed learning of governing equations from scarce data | |
Ma et al. | DeePr-ESN: A deep projection-encoding echo-state network | |
Wiewel et al. | Latent space physics: Towards learning the temporal evolution of fluid flow | |
Fablet et al. | Learning variational data assimilation models and solvers | |
Lee et al. | PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry | |
Ruhe et al. | Clifford group equivariant neural networks | |
Makantasis et al. | Rank-r fnn: A tensor-based learning model for high-order data classification | |
Peyvan et al. | RiemannONets: Interpretable neural operators for Riemann problems | |
Tran et al. | Weak-form latent space dynamics identification | |
Liu et al. | Machine-learning-based prediction of regularization parameters for seismic inverse problems | |
Taccari et al. | Developing a cost-effective emulator for groundwater flow modeling using deep neural operators | |
Doherty et al. | QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression | |
Tang et al. | Solving high-dimensional Fokker-Planck equation with functional hierarchical tensor | |
Franco et al. | Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction | |
Srivastava et al. | Generative and discriminative training of Boltzmann machine through quantum annealing | |
Li et al. | Infinite-fidelity coregionalization for physical simulation | |
Rajendran et al. | Data mining when each data point is a network | |
Xiao et al. | Distributed gauss-newton optimization with smooth local parameterization for large-scale history-matching problems | |
Chakraborty et al. | Multigoal-oriented dual-weighted-residual error estimation using deep neural networks | |
Xia et al. | VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems | |
Lu et al. | A diffusion‐based uncertainty quantification method to advance E3SM land model calibration | |
Maddu et al. | Learning fast, accurate, and stable closures of a kinetic theory of an active fluid | |
Lin et al. | Parallel Implementation of Ensemble Kalman Smoother for Field-Scale Assisted History Matching | |
Gupta | Solving Forward and Inverse Problems for Seismic Imaging using Invertible Neural Networks |