Presentation and discussion of a machine learning model to infer differential equations from data
Håkon Tasken, PhD student at UiO from the center for excellence SFF Integreat will present the paper:
ODEFORMER: SYMBOLIC REGRESSION OF DYNAMICAL SYSTEMS WITH TRANSFORMERS https://openreview.net/pdf?id=TzoHLiGVMo
He will put it in the context of machine learning approaches to find equations from data. He will then shortly explain the aim of his PhD related to these approaches.
Abstract of the paper: 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.