`Neural forecasting of high-dimensional dynamical Systems'

In this talk, I will present recent research that builds fast and accurate predictive neural-network models for various high-dimensional systems through a combination of data-driven and physics-based modeling. Moreover, through my examples, I will argue that such algorithm development must occur with consideration for data from multiple sources and fidelities and with appropriate emphasis on incorporating prior knowledge from physics-based modeling. I will give specific examples that highlight such themes for learning both canonical and off-nominal nonlinear dynamical systems from data. Some examples of the former include learning solutions to the advection-dominated viscous Burgers equations, and learning the chaotic nature of the Kuramoto-Sivashinsky equation. For the latter, I will discuss deployments of such learning algorithms for building reduced-order models for geophysical forecasting from numerical simulations as well as ship and satellite observation data.