ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
Stéphane d'Ascoli Sören Becker Alexander Mathis Philippe Schwaller Niki Kilbertus
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 dataset publicly.