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This code is an improved implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression, Silviu-Marian Udrescu and Max Tegmark (2019) [[Science Advances](https://advances.sciencemag.org/content/6/16/eaay2631/tab-pdf)] and AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity, Udrescu S.M. et al. (2020) [[arXiv](https://arxiv.org/abs/2006.10782)]. This code is an improved implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression, Silviu-Marian Udrescu and Max Tegmark (2019) [[Science Advances](https://advances.sciencemag.org/content/6/16/eaay2631/tab-pdf)] and AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity, Udrescu S.M. et al. (2020) [[arXiv](https://arxiv.org/abs/2006.10782)].
Please check [[this Medium article](https://towardsdatascience.com/ai-feynman-2-0-learning-regression-equations-from-data-3232151bd929)] for a more detailed eplanation of how to get the code running. Please check [this Medium article](https://towardsdatascience.com/ai-feynman-2-0-learning-regression-equations-from-data-3232151bd929) for a more detailed eplanation of how to get the code running.
In order to get started, run compile.sh to compile the fortran files used for the brute force code. In order to get started, run compile.sh to compile the fortran files used for the brute force code.