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Title: Advancing Fusion with Machine Learning Research Needs Workshop Report

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)more » 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. 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.« less

Authors:
ORCiD logo; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)
OSTI Identifier:
1668090
Alternate Identifier(s):
OSTI ID: 1670184; OSTI ID: 1716821; OSTI ID: 1780806
Report Number(s):
SAND-2020-6449J; LA-UR-20-24089
Journal ID: ISSN 0164-0313; PII: 258
Grant/Contract Number:  
AC04-94AL85000; FC02-04ER54698; AC52-07NA27344; NA0003525; 89233218CNA000001
Resource Type:
Published Article
Journal Name:
Journal of Fusion Energy
Additional Journal Information:
Journal Name: Journal of Fusion Energy Journal Volume: 39 Journal Issue: 4; Journal ID: ISSN 0164-0313
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY; fusion science; artificial intelligence; machine learning; scientific discovery

Citation Formats

Humphreys, David, Kupresanin, A., Boyer, M. D., Canik, J., Chang, C. S., Cyr, E. C., Granetz, R., Hittinger, J., Kolemen, E., Lawrence, E., Pascucci, V., Patra, A., and Schissel, D. Advancing Fusion with Machine Learning Research Needs Workshop Report. United States: N. p., 2020. Web. https://doi.org/10.1007/s10894-020-00258-1.
Humphreys, David, Kupresanin, A., Boyer, M. D., Canik, J., Chang, C. S., Cyr, E. C., Granetz, R., Hittinger, J., Kolemen, E., Lawrence, E., Pascucci, V., Patra, A., & Schissel, D. Advancing Fusion with Machine Learning Research Needs Workshop Report. United States. https://doi.org/10.1007/s10894-020-00258-1
Humphreys, David, Kupresanin, A., Boyer, M. D., Canik, J., Chang, C. S., Cyr, E. C., Granetz, R., Hittinger, J., Kolemen, E., Lawrence, E., Pascucci, V., Patra, A., and Schissel, D. Sat . "Advancing Fusion with Machine Learning Research Needs Workshop Report". United States. https://doi.org/10.1007/s10894-020-00258-1.
@article{osti_1668090,
title = {Advancing Fusion with Machine Learning Research Needs Workshop Report},
author = {Humphreys, David and Kupresanin, A. and Boyer, M. D. and Canik, J. and Chang, C. S. and Cyr, E. C. and Granetz, R. and Hittinger, J. and Kolemen, E. and Lawrence, E. and Pascucci, V. and Patra, A. and Schissel, D.},
abstractNote = {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. 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.},
doi = {10.1007/s10894-020-00258-1},
journal = {Journal of Fusion Energy},
number = 4,
volume = 39,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1007/s10894-020-00258-1

Figures / Tables:

Fig. II.1-1 Fig. II.1-1: Scientific Discovery with Machine Learning includes approaches to bridging gaps in theoretical understanding through the identification of missing effects using large datasets; accelerating hypothesis generation and testing; and optimizing experimental planning to help speed up progress in gaining new knowledge.

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