  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.
