Discovering physical laws with parallel symbolic enumeration.

ABSTRACT: Symbolic regression has a crucial role in modern scientific research owing to its capability of discovering concise and interpretable mathematical expressions from data. A key challenge lies in the 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. Here, to this end, we introduce parallel symbolic enumeration (PSE) to efficiently distill generic mathematical expressions from limited data. Experiments show that PSE achieves higher accuracy and faster c

SUBMITTER: Ruan K 

PROVIDER: S-EPMC12855010 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature