EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report)

Kruger, Scott E. [Tech-X Corp., Boulder, CO (United States)] (ORCID:0000000191746227); Howell, Eric [Tech-X Corp., Boulder, CO (United States)]

doi:10.2172/2484189

DOE OSTI · 2484189

EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report)

Kruger, Scott E. [Tech-X Corp., Boulder, CO (United States)] (ORCID:0000000191746227)·Howell, Eric [Tech-X Corp., Boulder, CO (United States)]

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Abstract

The EFIT-AI project is creating a modern advanced equilibrium reconstruction code suitable for tokamak experiments of burning plasmas. EFIT [1,2] was the first and is the most extensively used equilibrium reconstruction code in the world. This project builds on the production-level experience and adds key elements as follows. 1. A Model Order Reduction (MOR) version of the two-dimensional (2D) Grad-Shafranov equation solver (EFIT-MORNN) using physics-informed neural networks. 2. Improved optimization and data analysis capabilities using a Bayesian framework enhanced with machine learning. 3. A MOR version of the three-dimensional (3D) perturbed equilibrium reconstruction tool.

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70 PLASMA PHYSICS AND FUSION TECHNOLOGY bayesian statistics data analysis equilibrium reconstruction machine learning plasma experiment plasma physics tokamak

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BibTeX RIS

Kruger, Scott E. [Tech-X Corp., Boulder, CO (United States)] (ORCID:0000000191746227), Howell, Eric [Tech-X Corp., Boulder, CO (United States)]. 2024-12-31. EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report). https://doi.org/10.2172/2484189

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