Bulletin of the American Physical Society
64th Annual Meeting of the APS Division of Plasma Physics
Volume 67, Number 15
Monday–Friday, October 17–21, 2022; Spokane, Washington
Session CT02: Tutorial: Magnetic Confinement Fusion I
2:00 PM–3:00 PM,
Monday, October 17, 2022
Room: Ballroom 100 B
Chair: Rachel Myers, Univ. Wisconsin, Madison
Abstract: CT02.00001 : Interpretable Machine Learning Accelerating Fusion Research*
2:00 PM–3:00 PM
Presenter:
Cristina Rea
(Massachusetts Institute of Technology MI)
Authors:
Cristina Rea
(Massachusetts Institute of Technology MI)
Jinxiang Zhu
(Massachusetts Institute of Technology MI)
Robert S Granetz
(Massachusetts Institute of Technology MI)
Kevin J Montes
(NextEra Energy Inc)
Roy A Tinguely
(Massachusetts Institute of Technology)
Ryan M Sweeney
(MIT PSFC)
Nathan T Howard
(MIT)
Pablo Rodriguez-Fernandez
(MIT Plasma Science and Fusion Center)
Jayson L Barr
(General Atomics - San Diego)
Mark D Boyer
(Princeton Plasma Physics Laboratory)
Keith Erickson
(Princeton Plasma Physics Laboratory)
Andrew Maris
(Massachusetts Institute of Technology)
This tutorial will start with a general description of Artificial Intelligence, and then focus on the specifics of Machine Learning and Deep Learning paradigms. Particular attention will be given to reviewing ML techniques that guarantee an explainable and interpretable predictive output, thus enabling effective controllers for magnetically confined fusion plasmas [Barr 2021 NF 61 126019]. Transfer learning and domain adaptation will also be discussed, since a common need exists to understand how to extrapolate knowledge to devices yet to be built or to experiments with different statistical properties [Zhu 2021 Nucl. Fusion 61 114005, Gaffney 2021 PoP 26 082704]. Finally, several examples of data-driven fusion applications will be provided, with particular emphasis given to active ML research conducted at the DIII-D facility.
This work is supported by the U.S. DOE under Award(s) DE-FC02-99ER54512, DE-SC0014264, DE-SC0010720, DE-SC0010492, and DE-FC02-04ER54698.
*This work is supported by the U.S. DOE under Award(s) DE-FC02-99ER54512, DE-SC0014264, DE-SC0010720, DE-SC0010492, and DE-FC02-04ER54698.
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