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[ascl:2310.011] AI-Feynman: Symbolic regression algorithm

AI-Feynman fits analytical expressions to data sets via symbolic regression, mapping the target variable to different features supplied in the data array. Using a neural network with constraints in the number of parameters utilized, the code provides the ability to obtain analytical expressions for normalized features that are used to predict a Pareto-optimal target. AI-Feynman is robust in handling noisy data, recursively generating multidimensional symbolic expressions that match data from an unknown functions.

Code site:
https://github.com/SJ001/AI-Feynman
Used in:
https://ui.adsabs.harvard.edu/abs/2023PhRvD.107j3522K
Described in:
https://ui.adsabs.harvard.edu/abs/2020SciA....6.2631U https://ui.adsabs.harvard.edu/abs/2020arXiv200610782U
Bibcode:
2023ascl.soft10011U

Views: 841

ascl:2310.011
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