<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Symbolic Regression | Yufei Liu's Homepages</title><link>https://liu-yufei.github.io/tags/symbolic-regression/</link><atom:link href="https://liu-yufei.github.io/tags/symbolic-regression/index.xml" rel="self" type="application/rss+xml"/><description>Symbolic Regression</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Feb 2024 00:00:00 +0000</lastBuildDate><image><url>https://liu-yufei.github.io/media/icon_hu17033853472409137288.png</url><title>Symbolic Regression</title><link>https://liu-yufei.github.io/tags/symbolic-regression/</link></image><item><title>An Interpretable Approach to the Solutions of High-Dimensional Partial Differential Equations</title><link>https://liu-yufei.github.io/publication/hd-tlgp/</link><pubDate>Thu, 01 Feb 2024 00:00:00 +0000</pubDate><guid>https://liu-yufei.github.io/publication/hd-tlgp/</guid><description>&lt;p>The method transfers the structure of one-dimensional analytical solutions to higher-dimensional forms, then uses genetic programming and automatic differentiation to search for mathematically interpretable solutions that satisfy the governing equations and boundary conditions.&lt;/p></description></item></channel></rss>