Advancing Fusion with Machine Learning Research Needs Workshop Report

David Humphreys, A. Kupresanin, M. D. Boyer, J. Canik, C. S. Chang, E. C. Cyr, R. Granetz, J. Hittinger, E. Kolemen, E. Lawrence, V. Pascucci, A. Patra, D. Schissel

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

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 Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

Original languageEnglish
Pages (from-to)123-155
Number of pages33
JournalJournal of Fusion Energy
Volume39
Issue number4
DOIs
StatePublished - Aug 1 2020

Funding

Dealing with all aspects associated with the generation, movement, and analysis of large sets of data (“big data”) has emerged as a critical issue for fusion and plasma science research. This need is driven by new modes and opportunities of research coupled with the emergence of more powerful computers, and has led to rapid growth in the adoption of artificial intelligence (AI) techniques and methodologies, including Machine Learning (ML), in the research areas supported by the Department of Energy (DOE) Office of Science (SC) program in Fusion Energy Sciences (FES). Examples of big data science drivers for FES include: At the same time, the DOE SC program in Advanced Scientific Computing Research (ASCR) has been supporting foundational research in computer science and applied mathematics to develop robust ML and AI capabilities that address the needs of multiple SC programs. Work supported by U.S. Department of Energy under DE-FC02-04ER54698, DE-AC52-07NA27344, and DE-NA0003525. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The pursuit of fusion energy has required extensive experimental and theoretical science activities to develop the knowledge needed that will enable design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned to enable optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. Jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR), a workshop was organized to identify Priority Research Opportunities (PRO’s) for application of ML/AI methods to enable accelerated solution of fusion problems. The resulting “Advancing Fusion with Machine Learning Research Needs Workshop,” held in Gaithersburg, MD, April 30–May 2, 2019, brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, to identify key PRO’s.

Keywords

  • Artificial intelligence
  • Fusion science
  • Machine learning
  • Scientific discovery

Fingerprint

Dive into the research topics of 'Advancing Fusion with Machine Learning Research Needs Workshop Report'. Together they form a unique fingerprint.

Cite this