Discovering physical laws with parallel combinatorial tree search

• URL: http://arxiv.org/abs/2407.04405v3
• Date: Mon, 03 Mar 2025 03:39:50 GMT
• Title: Discovering physical laws with parallel combinatorial tree search
• Authors: Kai Ruan, Yilong Xu, Ze-Feng Gao, Yike Guo, Hao Sun, Ji-Rong Wen, Yang Liu,
• Abstract summary: Symbolic regression plays a crucial role in scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data.<n>Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade.<n>We introduce a parallel tree search (PCTS) model to efficiently distill generic mathematical expressions from limited data.
• Score: 57.05912962368898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A grand challenge lies in the arduous search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. To this end, we introduce a parallel combinatorial tree search (PCTS) model to efficiently distill generic mathematical expressions from limited data. Through a series of extensive experiments, we demonstrate the superior accuracy and efficiency of PCTS for equation discovery, which greatly outperforms the state-of-the-art baseline models on over 200 synthetic and experimental datasets (e.g., lifting its performance by up to 99% accuracy improvement and one-order of magnitude speed up). PCTS represents a key advance in accurate and efficient data-driven discovery of symbolic, interpretable models (e.g., underlying physical laws) and marks a pivotal transition towards scalable symbolic learning.

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