Advancing Fusion with Machine Learning Research Needs Workshop and Report

Data-driven machine learning (ML) methods have been applied to fusion energy research for over two decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. Advances in large-scale parallel computation and statistical inference mathematics, along with the need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven expansion of efforts in ML within the US government and worldwide. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML methods to fusion energy research. We describe the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30 – May 2, 2019, in Gaithersburg, MD (https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf). The workshop had broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO's) with high potential for advancing fusion energy.