f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990)

scientific article; zbMATH DE number 7922779

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English	f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain	scientific article; zbMATH DE number 7922779

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instance of

scholarly article

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title

f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (English)

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Journal of Computational Physics

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publication date

8 October 2024

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zbMATH Keywords

physics-informed convolutional neural network

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nonlinear convolutional units (NCUs)

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memory mechanism

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partial differential equations

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finite discretization

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auto-regressive model

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MaRDI profile type

Publication

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cites work

Prediction of aerodynamic flow fields using convolutional neural networks

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Model reduction and neural networks for parametric PDEs

0 references

Q4982602

0 references

DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks

0 references

Convolutional neural network based simulation and analysis for backward stochastic partial differential equations

0 references

Multilevel domain decomposition-based architectures for physics-informed neural networks

0 references

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

0 references

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

0 references

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

0 references

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

0 references

Neural-PDE: a RNN based neural network for solving time dependent PDEs

0 references

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

0 references

\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

0 references

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

0 references

DeepXDE: A Deep Learning Library for Solving Differential Equations

0 references

An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator

0 references

Self-adaptive physics-informed neural networks

0 references

PPINN: parareal physics-informed neural network for time-dependent PDEs

0 references

Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

0 references

fPINNs: Fractional Physics-Informed Neural Networks

0 references

A physics-informed operator regression framework for extracting data-driven continuum models

0 references

A New Treatment of Boundary Conditions in PDE Solution with Galerkin Methods via Partial Integral Equation Framework

0 references

A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions

0 references

Meta-learning PINN loss functions

0 references

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

0 references

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

0 references

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

0 references

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

0 references

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

0 references

Parallel physics-informed neural networks via domain decomposition

0 references

Q3696432

0 references

Loss-attentional physics-informed neural networks

0 references

DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations

0 references

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

0 references

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

0 references

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

0 references

Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems

0 references

Numerical approximation based on deep convolutional neural network for high-dimensional fully nonlinear merged PDEs and 2BSDEs

0 references

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

0 references

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

0 references

Adaptive deep neural networks methods for high-dimensional partial differential equations

0 references

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

0 references

Parallel physics-informed neural networks method with regularization strategies for the forward-inverse problems of the variable coefficient modified KdV equation

0 references

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

0 references

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

0 references

Q5177422

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2024.113284

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Sitelinks

Mathematics(1 entry)

mardi Publication:6614990