On generalized residual network for deep learning of unknown dynamical systems

DOI10.1016/J.JCP.2021.110362OpenAlexW3005071597MaRDI QIDQ2124404

Dongbin Xiu, Zhen Chen

Publication date: 11 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.02528

zbMATH Keywords

residual networkdeep neural networkmodel correctiongoverning equation discovery

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Partial differential equations of mathematical physics and other areas of application (35Qxx) Approximation methods and numerical treatment of dynamical systems (37Mxx)

Related Items (3)

Deep-OSG: deep learning of operators in semigroup ⋮ Learning the Dynamics for Unknown Hyperbolic Conservation Laws Using Deep Neural Networks ⋮ Correcting model misspecification in physics-informed neural networks (PINNs)

Uses Software

• TensorFlow
• PDE-Net

Cites Work

• Unnamed Item
• Unnamed Item
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Non-intrusive reduced order modeling of nonlinear problems using neural networks
• Machine learning of linear differential equations using Gaussian processes
• Hidden physics models: machine learning of nonlinear partial differential equations
• A machine learning approach for efficient uncertainty quantification using multiscale methods
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
• Numerical aspects for approximating governing equations using data
• Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
• Data driven governing equations approximation using deep neural networks
• Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
• Solving for high-dimensional committor functions using artificial neural networks
• Numerical strategy for model correction using physical constraints
• An artificial neural network as a troubled-cell indicator
• On dynamic mode decomposition: theory and applications
• Bayesian Calibration of Computer Models
• Dynamic mode decomposition of numerical and experimental data
• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
• Extracting Sparse High-Dimensional Dynamics from Limited Data
• Model selection for dynamical systems via sparse regression and information criteria
• Learning partial differential equations via data discovery and sparse optimization
• Combining Field Data and Computer Simulations for Calibration and Prediction
• Solving high-dimensional partial differential equations using deep learning
• POLYNOMIAL CHAOS FOR SEMIEXPLICIT DIFFERENTIAL ALGEBRAIC EQUATIONS OF INDEX 1
• Multiscale Methods

This page was built for publication: On generalized residual network for deep learning of unknown dynamical systems