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Interpretable machine learning in Physics

High Energy Physics - Phenomenology 2022-05-04 v3 Machine Learning
Authors: Christophe Grojean , Ayan Paul , Zhuoni Qian , Inga Strümke
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Abstract

Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.

Keywords

sociophysics and complexity science mathematical physics foundations of physics

Cite

@article{arxiv.2203.08021,
  title  = {Interpretable machine learning in Physics},
  author = {Christophe Grojean and Ayan Paul and Zhuoni Qian and Inga Strümke},
  journal= {arXiv preprint arXiv:2203.08021},
  year   = {2022}
}

Comments

Submitted version of invited Comment Article for Nature Reviews Physics (2022)

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