Machine Learning for Partial Differential Equations

Steven L. Brunton, J. Nathan Kutz
3/30/2023
cs.LGmath.APmath.DS

No official code found. Best implementation: maziarraissi/HPM (152 stars, MIT, last commit 2020-02-20).

Abstract

Partial differential equations (PDEs) are among the most universal and parsimonious descriptions of natural physical laws, capturing a rich variety of phenomenology and multi-scale physics in a compact and symbolic representation. This review will examine several promising avenues of PDE research that are being advanced by machine learning, including: 1) the discovery of new governing PDEs and coarse-grained approximations for complex natural and engineered systems, 2) learning effective coordinate systems and reduced-order models to make PDEs more amenable to analysis, and 3) representing solution operators and improving traditional numerical algorithms. In each of these fields, we summarize key advances, ongoing challenges, and opportunities for further development.

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Code Implementations(1)

maziarraissi/HPMCommunity71%
MIT

Hidden physics models: Machine learning of nonlinear partial differential equations

15279Java, MATLABDec 3, 20176 years agoMIT

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Cite this paper

@article{brunton2023machine,
  title  = {Machine Learning for Partial Differential Equations},
  author = {Steven L. Brunton and J. Nathan Kutz},
  year   = {2023},
  eprint = {2303.17078},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.17078v1}
}

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