f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain
type: Item
instance of: scholarly article
title: f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain
author: Biao Yuan
author: He Wang
author: Ana Heitor
author: Xiaohui Chen
published in: Journal of Computational Physics
publication date: 2024-10-08T00:00:00Z
Mathematics Subject Classification ID: 68Txx
Mathematics Subject Classification ID: 65Mxx
Mathematics Subject Classification ID: 65Nxx
zbMATH DE Number: 7922779
zbMATH Keywords: physics-informed convolutional neural network
zbMATH Keywords: nonlinear convolutional units (NCUs)
zbMATH Keywords: memory mechanism
zbMATH Keywords: partial differential equations
zbMATH Keywords: finite discretization
zbMATH Keywords: auto-regressive model
MaRDI profile type: Publication
cites work: Prediction of aerodynamic flow fields using convolutional neural networks
cites work: Model reduction and neural networks for parametric PDEs
cites work: Q4982602
cites work: DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks
cites work: Convolutional neural network based simulation and analysis for backward stochastic partial differential equations
cites work: Multilevel domain decomposition-based architectures for physics-informed neural networks
cites work: PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
cites work: Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
cites work: Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
cites work: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
cites work: Neural-PDE: a RNN based neural network for solving time dependent PDEs
cites work: Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
cites work: \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
cites work: PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
cites work: DeepXDE: A Deep Learning Library for Solving Differential Equations
cites work: An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator
cites work: Self-adaptive physics-informed neural networks
cites work: PPINN: parareal physics-informed neural network for time-dependent PDEs
cites work: Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
cites work: fPINNs: Fractional Physics-Informed Neural Networks
cites work: A physics-informed operator regression framework for extracting data-driven continuum models
cites work: A New Treatment of Boundary Conditions in PDE Solution with Galerkin Methods via Partial Integral Equation Framework
cites work: A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions
cites work: Meta-learning PINN loss functions
cites work: Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization
cites work: PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
cites work: On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
cites work: Parallel physics-informed neural networks via domain decomposition
cites work: Q3696432
cites work: Loss-attentional physics-informed neural networks
cites work: DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations
cites work: ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains
cites work: A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
cites work: Data-driven deep learning of partial differential equations in modal space
cites work: Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems
cites work: Numerical approximation based on deep convolutional neural network for high-dimensional fully nonlinear merged PDEs and 2BSDEs
cites work: Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
cites work: Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
cites work: Adaptive deep neural networks methods for high-dimensional partial differential equations
cites work: Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
cites work: Parallel physics-informed neural networks method with regularization strategies for the forward-inverse problems of the variable coefficient modified KdV equation
cites work: Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
cites work: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
cites work: Q5177422
DOI: 10.1016/J.JCP.2024.113284
description: scientific article; zbMATH DE number 7922779