Presentation #129.02 in the session Tracking Plasma Flows in the Heliosphere — Poster Session.
Our objective is to develop insight into the spatial properties of solar coronal heating using a statistical analysis of the emission from observed and simulated active regions. To this end, we perform data-driven MHD simulations of active regions. The MURaM simulation is being modified to work with photospheric inputs as boundary conditions, including observed vector magnetograms, and electric field maps and flow maps inferred from observations. We focus on photospheric flow maps derived through supervised deep learning. More specifically, we train a convolutional neural network to emulate the MURaM simulation and infer MURaM-like flows from observational data, including flows in the granulation surrounding active regions. We present derivations of boundary conditions (i.e., electric field maps, flows maps) from SDO/HMI observations of selected active regions, and discuss the limitations and challenges associated with the methods. We detail ongoing efforts in driving the MURaM simulation from derived boundary conditions. Finally, we illustrate how these data-driven simulations will be used to study the structuring of the emission of active regions statistically and identify which scenario of coronal heating best matches observations.