%0 Journal Article %1 doi:10.1126/sciadv.aay2631 %A Udrescu, Silviu-Marian %A Tegmark, Max %D 2020 %J Science Advances %K ak-symbolic-numeric app_physics %N 16 %P eaay2631 %R 10.1126/sciadv.aay2631 %T AI Feynman: A physics-inspired method for symbolic regression %U https://www.science.org/doi/abs/10.1126/sciadv.aay2631 %V 6 %X Our physics-inspired algorithm for symbolic regression is able to discover complex physics equations from mere tables of numbers. 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 Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90\%.

@article{doi:10.1126/sciadv.aay2631, abstract = {Our physics-inspired algorithm for symbolic regression is able to discover complex physics equations from mere tables of numbers. 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 Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90\%.}, added-at = {2024-01-22T09:30:03.000+0100}, author = {Udrescu, Silviu-Marian and Tegmark, Max}, biburl = {https://www.bibsonomy.org/bibtex/2a8e96edede4cb84ab0e8bcc011585bd9/martinr}, doi = {10.1126/sciadv.aay2631}, eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.aay2631}, interhash = {023c422bc0b34222f2e0439f7ed1eeaa}, intrahash = {a8e96edede4cb84ab0e8bcc011585bd9}, journal = {Science Advances}, keywords = {ak-symbolic-numeric app_physics}, number = 16, pages = {eaay2631}, timestamp = {2024-01-22T09:30:03.000+0100}, title = {AI Feynman: A physics-inspired method for symbolic regression}, url = {https://www.science.org/doi/abs/10.1126/sciadv.aay2631}, volume = 6, year = 2020 }