Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy

Lucas Spangher, Allen M. Wang, Andrew Maris, Myles Stapelberg, Viraj Mehta, Alex Saperstein, Stephen Lane-Walsh, Akshata Kishore Moharir, Alessandro Pau, Cristina Rea

Abstract: Magnetic confinement fusion may one day provide reliable, carbon-free energy, but the field currently faces technical hurdles. In this position paper, we highlight six key research challenges in the field of fusion energy that we believe should be research priorities for the Machine Learning (ML) community because they are especially ripe for ML applications: (1) disruption prediction, (2) simulation and dynamics modeling (3) resolving partially observed data, (4) improving controls, (5) guiding experiments with optimal design, and (6) enhancing materials discovery. For each problem, we give background, review past ML work, suggest features of future models, and list challenges and idiosyncrasies facing ML development. We also discuss ongoing efforts to update the fusion data ecosystem and identify opportunities further down the line that will be enabled as fusion and its data infrastructure advance. It is our position that fusion energy offers especially exciting opportunities for ML practitioners to impact decarbonization and the future of energy.

Submission Number: 8582