Predicting Ordinary Differential Equations with Transformers
Su00f6ren Becker,u00a0Michal Klein,u00a0Alexander Neitz,u00a0Giambattista Parascandolo,u00a0Niki Kilbertus
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observations of a single solution trajectory. We demonstrate in extensive empirical evaluations that our model performs better or on par with existing methods in terms of accurate recovery across various settings. Moreover, our method is efficiently scalable: after one-time pretraining on a large set of ODEs, we can infer the governing law of a new observed solution in a few forward passes of the model.
