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AI Feynman: A physics-inspired method for symbolic regression

Science advances, 2020-04, Vol.6 (16), p.eaay2631-eaay2631 [Peer Reviewed Journal]

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). ;Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). 2020 The Authors ;ISSN: 2375-2548 ;EISSN: 2375-2548 ;DOI: 10.1126/sciadv.aay2631 ;PMID: 32426452

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  • Title:
    AI Feynman: A physics-inspired method for symbolic regression
  • Author: Udrescu, Silviu-Marian ; Tegmark, Max
  • Subjects: Computer Science ; Physical Sciences ; SciAdv r-articles
  • Is Part Of: Science advances, 2020-04, Vol.6 (16), p.eaay2631-eaay2631
  • Description: A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the , and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.
  • Publisher: United States: American Association for the Advancement of Science
  • Language: English
  • Identifier: ISSN: 2375-2548
    EISSN: 2375-2548
    DOI: 10.1126/sciadv.aay2631
    PMID: 32426452
  • Source: PubMed Central
    DOAJ Directory of Open Access Journals
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