Advancing Fusion with Machine Learning Research Needs Workshop
- General Atomics, La Jolla, CA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Princeton Univ., NJ (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of Utah, Salt Lake City, UT (United States)
- State Univ. of New York (SUNY), Albany, NY (United States)
- General Atomics, San Diego, CA (United States)
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.
- Research Organization:
- USDOE Office of Science (SC) (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI ID:
- 1615240
- Resource Relation:
- Conference: Advancing Fusion with Machine Learning Research Needs Workshop, Gaithersburg, MD (United States), 30 Apr - 2 May 2019
- Country of Publication:
- United States
- Language:
- English
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