Param-Intelligence Seminar Series, Neural ordinary differential equations for scientific machine learning by Romit Maulik, Pennsylvania State University (virtual)

In this talk, I will discuss recent advancements in the construction and training of neural
ordinary differential equations (Neural ODEs) for learning complex dynamical systems characterized
by chaotic and multiscale behavior. In particular, my talk will discuss the challenges associated with
learning invariant statistics of dynamical systems given solely short-term predictive performance
based objective functions. We will introduce neural architectures and algorithms in the neural ODE
paradigm that leverage partial knowledge of the underlying dynamics to obtain surrogate models
that can recover correct chaotic dynamics for a wide range of systems ranging from the 1D Kuramoto-
Sivashinsky equations to turbulent Navier-Stokes equations and the planetary atmosphere and ocean
systems.