Discovering Symmetries of ODEs by Symbolic Regression

Fuente: arXiv
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Autori principali: Kahlmeyer, Paul, Merk, Niklas, Giesen, Joachim
Natura: Preprint
Pubblicazione: 2025
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author Kahlmeyer, Paul
Merk, Niklas
Giesen, Joachim
author_facet Kahlmeyer, Paul
Merk, Niklas
Giesen, Joachim
contents Solving systems of ordinary differential equations (ODEs) is essential when it comes to understanding the behavior of dynamical systems. Yet, automated solving remains challenging, in particular for nonlinear systems. Computer algebra systems (CASs) provide support for solving ODEs by first simplifying them, in particular through the use of Lie point symmetries. Finding these symmetries is, however, itself a difficult problem for CASs. Recent works in symbolic regression have shown promising results for recovering symbolic expressions from data. Here, we adapt search-based symbolic regression to the task of finding generators of Lie point symmetries. With this approach, we can find symmetries of ODEs that existing CASs cannot find.
format Preprint
id arxiv_https___arxiv_org_abs_2506_19550
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Discovering Symmetries of ODEs by Symbolic Regression
Kahlmeyer, Paul
Merk, Niklas
Giesen, Joachim
Machine Learning
Solving systems of ordinary differential equations (ODEs) is essential when it comes to understanding the behavior of dynamical systems. Yet, automated solving remains challenging, in particular for nonlinear systems. Computer algebra systems (CASs) provide support for solving ODEs by first simplifying them, in particular through the use of Lie point symmetries. Finding these symmetries is, however, itself a difficult problem for CASs. Recent works in symbolic regression have shown promising results for recovering symbolic expressions from data. Here, we adapt search-based symbolic regression to the task of finding generators of Lie point symmetries. With this approach, we can find symmetries of ODEs that existing CASs cannot find.
title Discovering Symmetries of ODEs by Symbolic Regression
topic Machine Learning
url https://arxiv.org/abs/2506.19550