Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree searchDownload PDF


22 Sept 2022, 12:34 (modified: 18 Nov 2022, 09:04)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: symbolic regression, Monte Carlo tree search, governing equations, nonlinear dynamics
TL;DR: Proposed a novel Symbolic Physics Learner (SPL) machine to discover the mathematical structure of nonlinear dynamics based on limited measurement data.
Abstract: Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Learner (SPL) machine to discover the mathematical structure of nonlinear dynamics. The key concept is to interpret mathematical operations and system state variables by computational rules and symbols, establish symbolic reasoning of mathematical formulas via expression trees, and employ a Monte Carlo tree search (MCTS) agent to explore optimal expression trees based on measurement data. The MCTS agent obtains an optimistic selection policy through the traversal of expression trees, featuring the one that maps to the arithmetic expression of underlying physics. Salient features of the proposed framework include search flexibility and enforcement of parsimony for discovered equations. The efficacy and superiority of the PSL machine are demonstrated by numerical examples, compared with state-of-the-art baselines.
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