Discovering Symmetries of ODEs by Symbolic Regression
Abstract
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.
Cite
Text
Kahlmeyer et al. "Discovering Symmetries of ODEs by Symbolic Regression." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I17.33948Markdown
[Kahlmeyer et al. "Discovering Symmetries of ODEs by Symbolic Regression." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/kahlmeyer2025aaai-discovering/) doi:10.1609/AAAI.V39I17.33948BibTeX
@inproceedings{kahlmeyer2025aaai-discovering,
title = {{Discovering Symmetries of ODEs by Symbolic Regression}},
author = {Kahlmeyer, Paul and Merk, Niklas and Giesen, Joachim},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {17715-17723},
doi = {10.1609/AAAI.V39I17.33948},
url = {https://mlanthology.org/aaai/2025/kahlmeyer2025aaai-discovering/}
}