  We present a machine learning based method for learning first integrals of
systems of ordinary differential equations from given trajectory data. The
method is model-agnostic in that it does not require explicit knowledge of the
underlying system of differential equations that generated the trajectories. As
a by-product, once the first integrals have been learned, also the system of
differential equations will be known. We illustrate our method by considering
several classical problems from the mathematical sciences.
