  The laws of physics have been written in the language of dif-ferential
equations for centuries. Neural Ordinary Differen-tial Equations (NODEs) are a
new machine learning architecture which allows these differential equations to
be learned from a dataset. These have been applied to classical dynamics
simulations in the form of Lagrangian Neural Net-works (LNNs) and Second Order
Neural Differential Equations (SONODEs). However, they either cannot represent
the most general equations of motion or lack interpretability. In this paper,
we propose Modular Neural ODEs, where each force component is learned with
separate modules. We show how physical priors can be easily incorporated into
these models. Through a number of experiments, we demonstrate these result in
better performance, are more interpretable, and add flexibility due to their
modularity.
