Pre-training strategy for solving evolution equations based on physics-informed neural networks (Q6107095)

scientific article; zbMATH DE number 7705900

Language	Label	Description	Also known as
English	Pre-training strategy for solving evolution equations based on physics-informed neural networks	scientific article; zbMATH DE number 7705900

Statements

instance of

scholarly article

0 references

title

Pre-training strategy for solving evolution equations based on physics-informed neural networks (English)

0 references

arXiv ID

2212.00798

0 references

DOI

10.1016/j.jcp.2023.112258

0 references

0 references

0 references

0 references

0 references

Journal of Computational Physics

0 references

publication date

3 July 2023

0 references

full work available at URL

https://arxiv.org/abs/2212.00798

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

pre-training

0 references

physics informed neural networks

0 references

evolution equation

0 references

MaRDI profile type

MaRDI publication profile

0 references

OpenAlex ID

W4379141442

0 references

cites work

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

0 references

DeepXDE: A Deep Learning Library for Solving Differential Equations

0 references

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

0 references

Physics-informed PointNet: a deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries

0 references

Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

0 references

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

0 references

fPINNs: Fractional Physics-Informed Neural Networks

0 references

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

0 references

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

0 references

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

0 references

A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations

0 references

When and why PINNs fail to train: a neural tangent kernel perspective

0 references

Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

0 references

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

0 references

Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

0 references

Weak adversarial networks for high-dimensional partial differential equations

0 references

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

0 references

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

0 references

Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

0 references

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

0 references

A Limited Memory Algorithm for Bound Constrained Optimization

0 references

Q5491447

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6107095