Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244)

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English	Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network	scientific article

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instance of

scholarly article

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title

Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (English)

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DOI

10.1016/j.jcp.2021.110928

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Journal of Computational Physics

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publication date

4 May 2022

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Mathematics Subject Classification ID

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partial differential equations

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discrete dynamic system

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convolutional neural network

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data-driven method

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PDEs discovering

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describes a project that uses

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2021.110928

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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 Identification of Parametric Partial Differential Equations

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Data-driven discovery of PDEs in complex datasets

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Data driven approximation of parametrized PDEs by reduced basis and neural networks

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

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An artificial neural network framework for reduced order modeling of transient flows

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A Data-Driven Approach for Multiscale Elliptic PDEs with Random Coefficients Based on Intrinsic Dimension Reduction

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ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

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

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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Neural algorithm for solving differential equations

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DGM: a deep learning algorithm for solving partial differential equations

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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A derivative-free method for solving elliptic partial differential equations with deep neural networks

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Solving high-dimensional partial differential equations using deep learning

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A mesh-free method for interface problems using the deep learning approach

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Multilayer feedforward networks are universal approximators

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Universality of deep convolutional neural networks

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PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

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Exponential integrators

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Exponential time differencing for stiff systems

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Q3696432

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An in-depth numerical study of the two-dimensional Kuramoto–Sivashinsky equation

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Mathematics(1 entry)

mardi Publication:2135244