A Mathematical View of the EFIT-AI Project
POSTER
Abstract
The EFIT-AI project will create a modern advanced equilibrium reconstruction code capable of meeting the needs of tokamaks with burning plasmas. Mathematically, equilibrium reconstruction is an inverse problem, where one is seeking to use the data to infer underlying physical properties through the use of mathematical models for the forward problem. Inverse problems have enjoyed a renaissance of mathematical interest in recent years thanks to advances in theories in uncertainty quantification and machine learning. These advances are at the intersection of statistics, functional analysis, computer science, and physics. Here, we review some of these advances in the context of the equilibrium reconstruction problem.
Presenters
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Scott E Kruger
Tech-X Corp, Tech-X
Authors
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Scott E Kruger
Tech-X Corp, Tech-X
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Eric Howell
Tech-X Corporation, Tech-X, Tech X Corporation
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Jarrod Leddy
Tech-X Corp, Tech-X
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Lang L Lao
General Atomics - San Diego, General Atomics
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Cihan Akcay
General Atomics
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Torrin A Bechtel
ORAU, GA, Orau, General Atomics / ORAU, University of Wisconsin - Madison
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Joseph T Mcclenaghan
General Atomics, General Atomics - San Diego, Oak Ridge National Laboratory
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Sandeep Madireddy
Argonne National Lab, Argonne National Laboratory, ANL
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Jaehoon Koo
Argonne National Laboratory, ANL
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Samuel W Williams
LBNL, Lawrence Berkeley National Laboratory
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Matthew Leinhauser
LBNL, LBNL, UDEL, Lawrence Berkeley National Laboratory
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Alexei Pankin
Princeton Plasma Physics Laboratory, PPPL