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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

  • Scott E Kruger

    Tech-X Corp, Tech-X

Authors

  • Scott E Kruger

    Tech-X Corp, Tech-X

  • Eric Howell

    Tech-X Corporation, Tech-X, Tech X Corporation

  • Jarrod Leddy

    Tech-X Corp, Tech-X

  • Lang L Lao

    General Atomics - San Diego, General Atomics

  • Cihan Akcay

    General Atomics

  • Torrin A Bechtel

    ORAU, GA, Orau, General Atomics / ORAU, University of Wisconsin - Madison

  • Joseph T Mcclenaghan

    General Atomics, General Atomics - San Diego, Oak Ridge National Laboratory

  • Sandeep Madireddy

    Argonne National Lab, Argonne National Laboratory, ANL

  • Jaehoon Koo

    Argonne National Laboratory, ANL

  • Samuel W Williams

    LBNL, Lawrence Berkeley National Laboratory

  • Matthew Leinhauser

    LBNL, LBNL, UDEL, Lawrence Berkeley National Laboratory

  • Alexei Pankin

    Princeton Plasma Physics Laboratory, PPPL