Physics-Informed Neural Networks and Extensions

Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi, George Em Karniadakis
8/29/2024
cs.LGcs.AI

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

PinnDE: Physics-Informed Neural Networks for Solving Differential Equations

88% match1 repo302024

On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it

88% match2023

Adaptive Physics-informed Neural Networks: A Survey

88% match2025

Solving differential equations using physics informed deep learning: a hand-on tutorial with benchmark tests

87% match1 repo122023

Densely Multiplied Physics Informed Neural Networks

87% match2024

Element-wise Multiplication Based Deeper Physics-Informed Neural Networks

87% match2024

Cite this paper

@article{raissi2024physicsinformed,
  title  = {Physics-Informed Neural Networks and Extensions},
  author = {Maziar Raissi and Paris Perdikaris and Nazanin Ahmadi and George Em Karniadakis},
  year   = {2024},
  eprint = {2408.16806},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.16806v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2408.16806.svg)](https://codeofpaper.com/paper/2408.16806)

Discussion