ArticleScientific reports2023
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Beyond empirical models: Discovering constitutive laws in solids with graph-based equation discovery.Science advances · 2026Article
- Discovering multiscale deep formulas in complex systems via neural-guided lambda calculus.Nature communications · 2026Article
- Moving from table to graph in physics-informed spatio-temporal symbolic regression.Scientific reports · 2026Article
- Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.Chemical research in toxicology · 2026Article
- Knowledge integration for physics-informed symbolic regression using pre-trained large language models.Scientific reports · 2026Article
- Mechanism-aware and explainable artificial intelligence for drug release prediction in polymeric drug delivery systems.Frontiers in medicine · 2026Article
- Computational investigation of the social function of domestic cat facial signals.Scientific reports · 2024Article
- Exploration-exploitation model of moth-inspired olfactory navigation.Journal of the Royal Society, Interface · 2024Article
- Using machine learning to find exact analytic solutions to analytically posed physics problems.Heliyon · 2024Article
- Review
- Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer.Frontiers in medicine · 2024Article
- Automated landmark-based cat facial analysis and its applications.Frontiers in veterinary science · 2024Article
- Digitally-enhanced dog behavioral testing.Scientific reports · 2023Article
- BrachySound: machine learning based assessment of respiratory sounds in dogs.Scientific reports · 2023Article
- Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders.Frontiers in research metrics and analytics · 2023Article
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Authors and funding
3 authors.
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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.
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