Extended dynamic mode decomposition for inhomogeneous problems (Q2132641)

scientific article; zbMATH DE number 7515451

Language	Label	Description	Also known as
English	Extended dynamic mode decomposition for inhomogeneous problems	scientific article; zbMATH DE number 7515451

Statements

instance of

scholarly article

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title

Extended dynamic mode decomposition for inhomogeneous problems (English)

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author

Hannah Lu

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Daniel M. Tartakovsky

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published in

Journal of Computational Physics

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publication date

28 April 2022

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full work available at URL

https://arxiv.org/abs/2004.06205

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zbMATH Keywords

reduced-order model

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data-driven

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learning

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inhomogeneous PDE

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residual network

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describes a project that uses

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems

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Learning partial differential equations via data discovery and sparse optimization

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Data-driven discovery of coarse-grained equations

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A data-driven approximation of the koopman operator: extending dynamic mode decomposition

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On convergence of extended dynamic mode decomposition to the Koopman operator

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Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator

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Extracting Sparse High-Dimensional Dynamics from Limited Data

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Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

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Dynamic mode decomposition of numerical and experimental data

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On dynamic mode decomposition: theory and applications

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Applied Koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems

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Prediction Accuracy of Dynamic Mode Decomposition

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Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena

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Data driven governing equations approximation using deep neural networks

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Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

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PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network

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Data-driven deep learning of partial differential equations in modal space

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Non-intrusive reduced order modeling of nonlinear problems using neural networks

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A deep-learning-based geological parameterization for history matching complex models

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A machine learning approach for efficient uncertainty quantification using multiscale methods

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Dynamic mode decomposition with control

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Higher Order Dynamic Mode Decomposition

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Q4236291

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Q2957673

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Spectral analysis of nonlinear flows

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Model Reduction for Flow Analysis and Control

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Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

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Generalizing Koopman Theory to Allow for Inputs and Control

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Data-Driven Model Predictive Control using Interpolated Koopman Generators

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Analysis of Fluid Flows via Spectral Properties of the Koopman Operator

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2021.110550

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Sitelinks

Mathematics(1 entry)

mardi Publication:2132641