Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
- General Atomics, San Diego, CA (United States)
- TechX, Boulder, CO (United States)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- General Atomics, San Diego, CA (United States); Oak Ridge Associated Univ., Oak Ridge, TN (United States)
- Univ. of Delaware, Newark, DE (United States)
- Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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.
- Research Organization:
- General Atomics, San Diego, CA (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- Grant/Contract Number:
- SC0021203; FC02-04ER54698; FG02-95ER54309; AC02-06CH11357; AC02-05CH11231
- OSTI ID:
- 1873871
- Alternate ID(s):
- OSTI ID: 1893820; OSTI ID: 1962799
- Report Number(s):
- DOE-GA-21203; TRN: US2306627
- Journal Information:
- Plasma Physics and Controlled Fusion, Vol. 64, Issue 7; ISSN 0741-3335
- Publisher:
- IOP ScienceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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