Computational and Applied Mathematics: Daniel Serino, Los Alamos National Lab, Structure-Preserving Machine Learning for Dynamical Systems
ZoomDeveloping robust and accurate data-based models for dynamical systems originating from plasma physics and hydrodynamics is of paramount importance. These applications pose several challenges, including the presence of multiple scales in time and space and a limited number of data, which is often noisy or inconsistent. The aim of structure-preserving ML is to strongly enforce…