Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime

Understand

The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems.

• The present paper offers a probabilistic perspective that simultaneously identifies predictive, lower-dimensional coarse-grained (CG) variables as well as their dynamics.
• We make use of the expressive ability of deep neural networks in order to represent the right-hand side of the CG evolution law.
• Furthermore, we demonstrate how domain knowledge that is very often available in the form of physical constraints (e.g.