  Natural laws are often described through differential equations yet finding a
differential equation that describes the governing law underlying observed data
is a challenging and still mostly manual task. In this paper we make a step
towards the automation of this process: we propose a transformer-based
sequence-to-sequence model that recovers scalar autonomous ordinary
differential equations (ODEs) in symbolic form from time-series data of a
single observed solution of the ODE. Our method is efficiently scalable: after
one-time pretraining on a large set of ODEs, we can infer the governing laws of
a new observed solution in a few forward passes of the model. Then we show that
our model performs better or on par with existing methods in various test cases
in terms of accurate symbolic recovery of the ODE, especially for more complex
expressions.
