Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
L. L. Lao, Scott Kruger, C. Akçay, Prasanna Balaprakash, T. A. Bechtel, E. C. Howell, Jaehoon Koo, J. Leddy, Matthew Leinhauser, Yueqiang Liu, Sandeep Madireddy, J. McClenaghan, D Orozco, A.Y. Pankin, D. P. Schissel, S. P. Smith, X. Sun, Samuel Williams
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.