SciMED: A Computational Framework For Physics-Informed Symbolic Regression with Scientist-In-The-Loop

• URL: http://arxiv.org/abs/2209.06257v1
• Date: Tue, 13 Sep 2022 18:31:23 GMT
• Title: SciMED: A Computational Framework For Physics-Informed Symbolic Regression with Scientist-In-The-Loop
• Authors: Liron Simon Keren, Alex Liberzon, Teddy Lazebnik
• Abstract summary: We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED) SciMED integrates scientific discipline wisdom in a scientist-in-the-loop approach with state-of-the-art symbolic regression methods. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from noisy data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discovering a meaningful, dimensionally homogeneous, 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 genetic algorithm-based wrapper selection method with automatic machine learning and two levels of SR methods. We test SciMED on four configurations of the settling of a sphere with and without a non-linear aerodynamic drag force. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from noisy data. Our results indicate better performance on these tasks than the state-of-the-art SR software package.

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