Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction. (1st July 2022)

• Record Type: Journal Article
• Authors: Lao, L L; Kruger, S; Akcay, C; Balaprakash, P; Bechtel, T A; Howell, E; Koo, J; Leddy, J; Leinhauser, M; Liu, Y Q; Madireddy, S; McClenaghan, J; Orozco, D; Pankin, A; Schissel, D; Smith, S; Sun, X; Williams, S
• Is Part Of: Plasma physics and controlled fusion. Volume 64:Number 7(2022)
• Publication Date: 2022-07-01
• Subjects: tokamak equilibrium reconstruction -- machine learning -- artificial intelligence -- Gaussian process -- model order reduction -- neural network -- 3D perturbed equilibrium; Plasma (Ionized gases) -- Periodicals; Controlled fusion -- Periodicals; 530.44
• DOI: 10.1088/1361-6587/ac6fff
• Languages: English
• ISSNs: 0741-3335

Abstract

Abstract: Recent progress in the application of machine learning (ML)/artificial intelligence (AI) algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, motional Stark effect, and kinetic reconstruction data has been generated for developments of EFIT model-order-reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. A neural-network MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian process Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the Magnetohydrodynamic Resistive Spectrum - Feedback (MARS-F) code for developments of 3D-MOR surrogate models.