Leveraging AI-Driven Methodologies to Enable Sun-to-Earth Space Weather Modelling

One of the major challenges in space weather modeling is the computationally prohibitive range in scales between small pre-eruptive features and the large scale of the heliosphere. The computational challenge results in large-scale simulations typically either under-resolving the solar structures giving rise to space weather or being forced to contend with a numerical boundary condition in the solar corona, resulting in a substantial physical impact on the evolution of the simulation. This boundary condition is particularly daunting as observational constraints on the magnetic topology are notoriously difficult to directly obtain in the solar corona. Our proposed innovation is to replace this poorly constrained boundary condition with an AI emulation trained on a suite of simulations of solar eruptions. IRAD investment in this project will strengthen a proposed NASA/Department of Energy partnership through the Argonne National Laboratory Leadership Computing Facility’s AI Program for EXploration (APEX).

AI emulation of poorly-constrained solar coronal boundary conditions, trained on solar eruption simulation suite; eliminates under-resolved solar structures and uncertain numerical boundaries; computationally efficient Sun-to-Earth space weather simulation capability.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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