Physical Symbolic Optimization

• URL: http://arxiv.org/abs/2312.03612v1
• Date: Wed, 6 Dec 2023 16:56:28 GMT
• Title: Physical Symbolic Optimization
• Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
• Abstract summary: We present a framework for constraining the automatic sequential generation of equations to obey the rules of dimensional analysis by construction. Our symbolic regression algorithm achieves state-of-the-art results in contexts in which variables and constants have known physical units.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a framework for constraining the automatic sequential generation of equations to obey the rules of dimensional analysis by construction. Combining this approach with reinforcement learning, we built $\Phi$-SO, a Physical Symbolic Optimization method for recovering analytical functions from physical data leveraging units constraints. Our symbolic regression algorithm achieves state-of-the-art results in contexts in which variables and constants have known physical units, outperforming all other methods on SRBench's Feynman benchmark in the presence of noise (exceeding 0.1%) and showing resilience even in the presence of significant (10%) levels of noise.

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