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Physics-Informed Neural Networks and Extensions

Machine Learning 2024-09-02 v1 Artificial Intelligence
Authors: Maziar Raissi , Paris Perdikaris , Nazanin Ahmadi , George Em Karniadakis
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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.

Keywords

physics-informed neural networks

Cite

@article{arxiv.2408.16806,
  title  = {Physics-Informed Neural Networks and Extensions},
  author = {Maziar Raissi and Paris Perdikaris and Nazanin Ahmadi and George Em Karniadakis},
  journal= {arXiv preprint arXiv:2408.16806},
  year   = {2024}
}

Comments

Frontiers of Science Awards 2024

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