Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
- Title
- Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
- Author
- 구재훈
- Keywords
- tokamak equilibrium reconstruction; machine learning; artificial intelligence; Gaussian process; model order reduction; neural network; 3D perturbed equilibrium
- Issue Date
- 2022-06-06
- Publisher
- IOP PUBLISHING LTD
- Citation
- PLASMA PHYSICS AND CONTROLLED FUSION, v. 64, no 7, page. 1-16
- 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.
- URI
- https://iopscience.iop.org/article/10.1088/1361-6587/ac6fffhttps://repository.hanyang.ac.kr/handle/20.500.11754/191836
- ISSN
- 1361-6587
- DOI
- 10.1088/1361-6587/ac6fff
- Appears in Collections:
- COLLEGE OF BUSINESS AND ECONOMICS[E](경상대학) > BUSINESS ADMINISTRATION(경영학부) > Articles
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