Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 구재훈 | - |
dc.date.accessioned | 2024-08-26T02:20:22Z | - |
dc.date.available | 2024-08-26T02:20:22Z | - |
dc.date.issued | 2022-06-06 | - |
dc.identifier.citation | PLASMA PHYSICS AND CONTROLLED FUSION, v. 64, no 7, page. 1-16 | en_US |
dc.identifier.issn | 1361-6587 | en_US |
dc.identifier.uri | https://iopscience.iop.org/article/10.1088/1361-6587/ac6fff | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/191836 | - |
dc.description.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. | en_US |
dc.description.sponsorship | This material is based upon work supported by the US Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under Award(s) DESC0021203, DE-FC02-04ER54698, DE-FG02-95ER54309, and GA IR&D. | en_US |
dc.language | en_US | en_US |
dc.publisher | IOP PUBLISHING LTD | en_US |
dc.relation.ispartofseries | v. 64, no 7;1-16 | - |
dc.subject | tokamak equilibrium reconstruction | en_US |
dc.subject | machine learning | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | Gaussian process | en_US |
dc.subject | model order reduction | en_US |
dc.subject | neural network | en_US |
dc.subject | 3D perturbed equilibrium | en_US |
dc.title | Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction | en_US |
dc.type | Article | en_US |
dc.relation.no | 7 | - |
dc.relation.volume | 64 | - |
dc.identifier.doi | 10.1088/1361-6587/ac6fff | en_US |
dc.relation.page | 1-16 | - |
dc.relation.journal | PLASMA PHYSICS AND CONTROLLED FUSION | - |
dc.contributor.googleauthor | Lao, L L | - |
dc.contributor.googleauthor | Kruger, S | - |
dc.contributor.googleauthor | Akcay, C | - |
dc.contributor.googleauthor | Balaprakash, P | - |
dc.contributor.googleauthor | Bechtel, T A | - |
dc.contributor.googleauthor | Howell, E | - |
dc.contributor.googleauthor | Koo, J | - |
dc.contributor.googleauthor | Leddy, J | - |
dc.contributor.googleauthor | Leinhauser, M | - |
dc.contributor.googleauthor | Liu, Y Q | - |
dc.relation.code | 2022038949 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF BUSINESS AND ECONOMICS[E] | - |
dc.sector.department | SCHOOL OF BUSINESS ADMINISTRATION | - |
dc.identifier.pid | jaehoonkoo | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.