Scientific Machine Learning

Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well-suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist; however, each model is only useful in a certain regime and often limited by computational complexity. This is a particularly important problem for high energy density plasma experiments, where the plasma is often evolving through multiple plasma regimes (and thus multiple models) or where different parts of the system exist in different regimes. As a consequence, it is crucial to have methods that can incorporate information from multiple plasma regimes in a physically-consistent manner.

Sub-grid closure models in MHD turbulence and reconnection