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Title: Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019.

Abstract

The pursuit of fusion energy has led to extensive activities in both experimental and theoretical science. The central goal of all these activities has been to develop the knowledge necessary for the 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 in order to achieve 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. From April 30 to May 2 of 2019, a workshop on “Advancing Fusion with Machine Learning Research Needs” was held in Gaithersburg, MD, with joint support from the Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR) Programs. The workshop 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, with the goal of identifying Priority Research Opportunities (PROs) for the application of ML/AI methods tomore » accelerating progress on solving fusion problems. During the workshop, seven key PROs were identified. The description of each PRO includes relevant fusion problems, potentially useful ML/AI approaches, known gaps currently preventing the use of such approaches, and research guidelines to maximize the effective application of ML/AI methods to the PRO. The report containing the full description of the workshop outcomes is available at https://science.osti.gov/-/media/fes/pdf/workshopreports/FES_ASCR_Machine_Learning_Report.pdf« less

Publication Date:
Research Org.:
USDOE Office of Science (SC) (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Fusion Energy Sciences (FES) (SC-24)
OSTI Identifier:
1615241
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

. Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019.. United States: N. p., 2019. Web. doi:10.2172/1615241.
. Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019.. United States. doi:10.2172/1615241.
. Mon . "Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019.". United States. doi:10.2172/1615241. https://www.osti.gov/servlets/purl/1615241.
@article{osti_1615241,
title = {Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019.},
author = {},
abstractNote = {The pursuit of fusion energy has led to extensive activities in both experimental and theoretical science. The central goal of all these activities has been to develop the knowledge necessary for the 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 in order to achieve 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. From April 30 to May 2 of 2019, a workshop on “Advancing Fusion with Machine Learning Research Needs” was held in Gaithersburg, MD, with joint support from the Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR) Programs. The workshop 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, with the goal of identifying Priority Research Opportunities (PROs) for the application of ML/AI methods to accelerating progress on solving fusion problems. During the workshop, seven key PROs were identified. The description of each PRO includes relevant fusion problems, potentially useful ML/AI approaches, known gaps currently preventing the use of such approaches, and research guidelines to maximize the effective application of ML/AI methods to the PRO. The report containing the full description of the workshop outcomes is available at https://science.osti.gov/-/media/fes/pdf/workshopreports/FES_ASCR_Machine_Learning_Report.pdf},
doi = {10.2172/1615241},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {5}
}