Cheng et al., 2023 - Google Patents
Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate modelsCheng et al., 2023
View HTML- Document ID
- 13430586605449041878
- Author
- Cheng S
- Chen J
- Anastasiou C
- Angeli P
- Matar O
- Guo Y
- Pain C
- Arcucci R
- Publication year
- Publication venue
- Journal of Scientific Computing
External Links
Snippet
Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines …
- 238000010801 machine learning 0 title abstract description 33
Classifications
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
- G06N3/08—Learning 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- 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
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cheng et al. | Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models | |
Hennigh et al. | NVIDIA SimNet™: An AI-accelerated multi-physics simulation framework | |
US12050845B2 (en) | Estimating physical parameters of a physical system based on a spatial-temporal emulator | |
Wu et al. | Predicting effective diffusivity of porous media from images by deep learning | |
Kashinath et al. | Physics-informed machine learning: case studies for weather and climate modelling | |
Geneva et al. | Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks | |
Bukka et al. | Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models | |
Wang et al. | An expert's guide to training physics-informed neural networks | |
Wang et al. | Physics-guided deep learning for dynamical systems: A survey | |
Pawar et al. | A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence | |
US11720727B2 (en) | Method and system for increasing the resolution of physical gridded data | |
Gómez-Vargas et al. | Neural network reconstructions for the Hubble parameter, growth rate and distance modulus | |
Santos et al. | Development of the Senseiver for efficient field reconstruction from sparse observations | |
Sun et al. | Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation | |
Kherad et al. | Reduced order framework for convection dominant and pure diffusive problems based on combination of deep long short‐term memory and proper orthogonal decomposition/dynamic mode decomposition methods | |
Huang et al. | LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data | |
Saetta et al. | Uncertainty quantification in autoencoders predictions: Applications in aerodynamics | |
Lu et al. | An efficient bayesian method for advancing the application of deep learning in earth science | |
Cheng et al. | Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems | |
Zalavadia et al. | Parametric model order reduction for adaptive basis selection using machine learning techniques during well location opt | |
Calo et al. | Spatial air quality prediction in urban areas via message passing | |
Shi et al. | Towards complex dynamic physics system simulation with graph neural ordinary equations | |
Gupta et al. | A survey on solving and discovering differential equations using deep neural networks | |
Kocijan et al. | Surrogate modelling for the forecast of Seveso-type atmospheric pollutant dispersion | |
Gopalan et al. | A hierarchical spatiotemporal statistical model motivated by glaciology |