From 5f5ca198d28e2b3173aed52c45ca7bd90393d347 Mon Sep 17 00:00:00 2001 From: Silviu Marian Udrescu Date: Sun, 21 Jun 2020 21:22:10 -0400 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7dbb1ae..dc29181 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # AI-Feynman -This code is an improved implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression, Silviu-Marian Udrescu and Max Tegmark (2019) [[arXiv](https://arxiv.org/abs/1905.11481)][[Science Advances](https://advances.sciencemag.org/content/6/16/eaay2631/tab-pdf)]. +This code is an improved implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression, Silviu-Marian Udrescu and Max Tegmark (2019) [[arXiv](https://arxiv.org/abs/1905.11481)][[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. [[arXiv](https://arxiv.org/abs/2006.10782)]. Differently from the original paper (described in the mentioned paper), the new code doesn't output just one possible equation to describe the data, but a Pareto frontier of possible equations. Among other advantages, this approach allows the code to be more robust against noise and give good approximations to the actual equations, in case that one can't be found.