Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
This addresses the challenge of deriving interpretable physical equations from noisy data for researchers in physics and related fields, representing an incremental improvement by integrating units constraints into existing deep reinforcement learning methods.
The paper tackles the problem of automated discovery of physical laws from data by introducing a symbolic regression framework that enforces physical units consistency, achieving state-of-the-art performance with over 0.1% noise and robustness up to 10% noise on physics benchmarks.
Symbolic Regression is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed interest in such approaches, the development of symbolic regression methods has not been focused on physics, where we have important additional constraints due to the units associated with our data. Here we present $Φ$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints. Our system is built, from the ground up, to propose solutions where the physical units are consistent by construction. This is useful not only in eliminating physically impossible solutions, but because the "grammatical" rules of dimensional analysis restrict enormously the freedom of the equation generator, thus vastly improving performance. The algorithm can be used to fit noiseless data, which can be useful for instance when attempting to derive an analytical property of a physical model, and it can also be used to obtain analytical approximations to noisy data. We test our machinery on a standard benchmark of equations from the Feynman Lectures on Physics and other physics textbooks, achieving state-of-the-art performance in the presence of noise (exceeding 0.1%) and show that it is robust even in the presence of substantial (10%) noise. We showcase its abilities on a panel of examples from astrophysics.