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Machine learning in physics: a short guide

arXiv 2310.10368 ↗First posted 16 Oct 2023cs.LG cond-mat.stat-mech physics.app-phcited by 0

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Abstract (arXiv)

Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 65 references that are in the map. First posted on arXiv 16 Oct 2023.

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16 of 65 references.

References · 16

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
  3. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  4. 2018Model Evaluation, Model Selection, and Algorithm Selection in Machine Learningcited by 157
  5. 2018A high-bias, low-variance introduction to Machine Learning for physicistscited by 108
  6. 2020Transformers for Modeling Physical Systemscited by 105
  7. 2020AI Poincar'e: Machine Learning Conservation Laws from Trajectoriescited by 77
  8. 2019Optimization for deep learning: theory and algorithmscited by 70
  9. 2020A Bayesian machine scientist to aid in the solution of challenging scientific problemscited by 61
  10. 2017Machine Learning for the Geosciences: Challenges and Opportunitiescited by 53
  11. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  12. 2016Discovering Phase Transitions with Unsupervised Learningcited by 49
4 more
  1. 2017Active learning machine learns to create new quantum experimentscited by 41
  2. 2016Clustering Algorithms: A Comparative Approachcited by 28
  3. 2022Supervised Hebbian Learningcited by 16
  4. 2018Exploring galaxy evolution with generative modelscited by 4

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