ArticleScience advances2020
AI Feynman: A physics-inspired method for symbolic regression.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
What it found
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Who cites it
108 citing papers in PubMed.
- A Likelihood-Based Framework for Simultaneously Learning Both Noise and Growth Dynamics Using Biologically-Informed Neural Networks.Bulletin of mathematical biology · 2026Article
- Discovering how ice crystals grow using neural ODEs and symbolic regression.Science advances · 2026Article
- Beyond empirical models: Discovering constitutive laws in solids with graph-based equation discovery.Science advances · 2026Article
- Human-Constrained AI-Assisted Identification of Defect-Topology Relations for Mechanical Degradation in Porous Solids: A Discrete-Element Study.Materials (Basel, Switzerland) · 2026Article
- Domain Knowledge Constrained Symbolic Regression for Optimising Dermal Drug Formulations.Pharmaceutical research · 2026Article
- Discovering interpretable low-dimensional dynamics using maximum entropy.Physical review. E · 2026Article
- Dimensionally Consistent Formation Pressure Prediction from Borehole Sensing Data Using Pressure Ratio Symbolic Regression.Sensors (Basel, Switzerland) · 2026Article
- Article
- Explainable AI: learning from the learners.Nature communications · 2026Review
- Discovering multiscale deep formulas in complex systems via neural-guided lambda calculus.Nature communications · 2026Article
- Vertexformer: the interpretable predictive research on thermocapillary convection of large Prandtl number liquid bridges.NPJ microgravity · 2026Article
- AutomataGPT: Transformer-Based Forecasting and Ruleset Inference for Two-Dimensional Cellular Automata.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Moving from table to graph in physics-informed spatio-temporal symbolic regression.Scientific reports · 2026Article
- ASP-assisted symbolic regression for interpretable modelling of 3D laminar channel flow.Scientific reports · 2026Article
- Accurate predictive model of band gap with selected important features based on explainable machine learning.Scientific reports · 2026Article
- Strength performance of hexagonal, hollow, and perforated GFRP-Reinforced concrete units: experimental, numerical, and AI-based investigations.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
- SymRefine: A symbolic regression approach for refining and compressing neural networks.Neurocomputing · 2026Article
- Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.PLoS computational biology · 2026Article
- Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi-Variable Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
48 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.