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d'Ascoli,  S.* ; Becker, S. ; Mathis, A.* ; Schwaller, P.* ; Kilbertus, N.

ODEFormer: Symbolic regression of dynamical systems with transformers.

In: (12th International Conference on Learning Representations, ICLR 2024, 07-11 May 2024, Hybrid, Vienna). 2024.
Verlagsversion
We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory. We perform extensive evaluations on two datasets: (i) the existing 'Strogatz' dataset featuring two-dimensional systems; (ii) ODEBench, a collection of one- to four-dimensional systems that we carefully curated from the literature to provide a more holistic benchmark. ODEFormer consistently outperforms existing methods while displaying substantially improved robustness to noisy and irregularly sampled observations, as well as faster inference. We release our code, model and benchmark at https://github.com/sdascoli/odeformer. © 2024 12th International Conference on Learning Representations, ICLR 2024.
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Publikationstyp Artikel: Konferenzbeitrag
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
Konferenztitel 12th International Conference on Learning Representations, ICLR 2024
Konferzenzdatum 07-11 May 2024
Konferenzort Hybrid, Vienna
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530003-001
G-503800-004
Scopus ID 85195391635
Erfassungsdatum 2024-10-21
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