Advancing Fusion with Machine Learning Research Needs Workshop Report
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ORIGINAL RESEARCH
Advancing Fusion with Machine Learning Research Needs Workshop Report David Humphreys1 • A. Kupresanin2 • M. D. Boyer3 • J. Canik4 • C. S. Chang3 • E. C. Cyr5 • R. Granetz6 • J. Hittinger2 • E. Kolemen7 • E. Lawrence8 • V. Pascucci9 • A. Patra10 • D. Schissel1 Accepted: 9 September 2020 / Published online: 26 September 2020 The Author(s) 2020
Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. 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/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Disclaimer This report was prepared as an account of work Advancing Fusion with Machine Learning, held April 30– sponsored by an agency of the United States Government. May 2, 2019, in Gaithersburg, MD (full report available at Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, https://science.osti.gov/-/media/fes/pdf/workshop-reports/ express or implied, or assumes any legal liability or FES_ASCR_Machine_Learning_Report.pdf). The workresponsibility for the accuracy, completeness, or usefulness shop drew on broad representation from both FES and of any information, apparatus, product, or process disclosed, ASCR scientific communities, and identified seven Priority or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, Research Opportunities (PRO’s
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