Collaborative Research: GEM--Towards Developing Physics-informed Subgrid Models for Geospace MagnetoHydroDynamics (MHD) Simulations

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While simulating the interaction between the solar wind and magnetosphere system, scientists usually use numerical magnetohydrodynamics (MHD), a model of electrically conducting fluids that treats all interpenetrating particle species together as a single continuous medium. Increasingly, MHD models require very-high numerical resolution for realistic global magnetosphere simulations of multiscale plasma flows. To address this problem, this project will develop new parameterizations for an existing global magnetosphere MHD model with the data-driven discovery by physics-informed machine learning and stochastic modeling. The project will support an earlier career scientist in a senior personnel role. The main broader impact will be the improvement of MHD components of global magnetosphere models, leading to better modeling and prediction of space weather. The developed techniques are very general and can be adapted to other complex high-dimensional dynamical systems with benefits to other areas of science and engineering.To broaden the results and prove their robustness, a hierarchy of physical problems will be employed for dynamical simulations of several types of multiscale turbulent MHD flows by GAMERA, to ascend systematically by increasing the reference data complexity: (1) 2D simulation of Orszag-Tang vortex, (2) 2D simulation of the Kelvin-Helmholtz instability, (3) 3D simulation of bursty bulk flows in the near-Earth magnetotail. The following key spatiotemporal reference data will be diagnosed from benchmark high-resolution GAMERA model solutions: (i) distributions of subgrid (small-scale) and large-scale fields, (ii) subgrid-scale forcing that encapsulates induced feedbacks on the large-scale fields. Physics-informed machine learning and stochastic modeling will be used to develop prognostic models of subgrid-scales and induced forcing, coupled to large-scale flow simulated by the coarse-scale GAMERA. Skills of the developed subgrid-scale parameterizations will be formally and systematically evaluated by the comprehensive set of physics-informed metrics relevant to practical applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.