CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144)

scientific article; zbMATH DE number 7532555

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English	CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method	scientific article; zbMATH DE number 7532555

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instance of

scholarly article

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title

CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

27 May 2022

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full work available at URL

https://arxiv.org/abs/2110.15832

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zbMATH Keywords

physics-informed neural network

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training loss formulation

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Taylor series expansions

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coupled-automatic-numerical differentiation

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Navier-Stokes equations

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inverse problem

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describes a project that uses

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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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Neural algorithm for solving differential equations

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Solution of nonlinear ordinary differential equations by feedforward neural networks

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Neural‐network‐based approximations for solving partial differential equations

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization

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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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DGM: a deep learning algorithm for solving partial differential equations

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

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Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

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PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

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Adversarial uncertainty quantification in physics-informed neural networks

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

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NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations

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

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Physics-informed neural networks for high-speed flows

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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

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Deep learning of vortex-induced vibrations

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On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks

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SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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Q4558473

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Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems

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A nonlocal physics-informed deep learning framework using the peridynamic differential operator

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On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

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Unified formulation for compressible and incompressible flows by using multi-integrated moments. I: One-dimensional inviscid compressible flow

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A divergence-free-condition compensated method for incompressible Navier-Stokes equations

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An effective explicit pressure gradient scheme implemented in the two-level non-staggered grids for incompressible Navier-Stokes equations

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Evaluation of various high‐order‐accuracy schemes with and without flux limiters

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Numerical solutions of 2-D steady incompressible driven cavity flow at high Reynolds numbers

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An improved divergence-free-condition compensated method for solving incompressible flows on collocated grids

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High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method

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A numerical study of flow over a confined backward‐facing step

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Numerical solutions of 2-D steady incompressible flow over a backward-facing step. I: High Reynolds number solutions

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2022.114909

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Sitelinks

Mathematics(1 entry)

mardi Publication:2142144