A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Liron Simon Keren, Alex Liberzon, Teddy Lazebnik

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.

Original languageEnglish
Article number1249
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - 23 Jan 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

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