| Deep neural network Grad–Shafranov solver constrained with measured magnetic signals S Joung, J Kim, S Kwak, JG Bak, SG Lee, HS Han, HS Kim, G Lee, ... Nuclear Fusion 60 (1), 016034, 2019 | 50 | 2019 |
| GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation S Joung, YC Ghim, J Kim, S Kwak, D Kwon, C Sung, D Kim, HS Kim, ... Scientific Reports 13 (1), 15799, 2023 | 9 | 2023 |
| Imputation of faulty magnetic sensors with coupled Bayesian and Gaussian processes to reconstruct the magnetic equilibrium in real time S Joung, J Kim, S Kwak, K Park, SH Hahn, HS Han, HS Kim, JG Bak, ... Review of Scientific Instruments 89 (10), 2018 | 7 | 2018 |
| Development of machine learning model for automatic ELM-burst detection without hyperparameter adjustment in KSTAR tokamak J Song, S Joung, YC Ghim, S Hahn, J Jang, J Lee Nuclear Engineering and Technology 55 (1), 100-108, 2023 | 5 | 2023 |
| Tokamak edge localized mode onset prediction with deep neural network and pedestal turbulence S Joung, DR Smith, G McKee, Z Yan, K Gill, J Zimmerman, B Geiger, ... Nuclear Fusion 64 (6), 066038, 2024 | 3 | 2024 |
| Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements K Gill, D Smith, S Joung, B Geiger, G McKee, J Zimmerman, R Coffee, ... Machine Learning: Science and Technology 5 (3), 035012, 2024 | 1 | 2024 |
| Automatic identification of edge localized modes in the DIII-D tokamak FH O’Shea, S Joung, DR Smith, R Coffee APL Machine Learning 1 (2), 2023 | 1 | 2023 |
| Non-monotonic radial structures of fluctuating temperatures and densities associated with fishbone activities in KSTAR W Lee, J Kim, S Joung, GJ Choi, J Kim, M Woo, T Rhee, KD Lee, JG Bak, ... Physics of Plasmas 30 (2), 2023 | 1 | 2023 |
| Bayesian neural network for plasma equilibria in the Korea Superconducting Tokamak Advanced Research S Joung arXiv preprint arXiv:2301.11555, 2023 | 1 | 2023 |
| Real-Time Detection of Confinement Regimes in Fusion Plasmas via Deep Learning and Edge Turbulence Measurements K Gill, D Smith, S Joung, B Geiger, G McKee, J Zimmerman, R Coffee, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Leveraging the MHz-scale 2D BES diagnostic in real time to predict the ELM onset based on deep learning acceleration S Joung, D Smith, K Gill, G McKee, Z Yan, B Geiger, J Zimmerman, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Kinetic profile inference with outlier detection using Support vector machine regression and Gaussian process regression M Kim, WH Ko, S Kwak, S Joung, W Lee, B Kim, D Kim, J Lee, C Sung, ... Nuclear Fusion, 2024 | | 2024 |
| DIII-D research to provide solutions for ITER and fusion energy CT Holcomb, J Abbate, A Abe, A Abrams, P Adebayo-Ige, S Agabian, ... Nuclear Fusion 64 (11), 112003, 2024 | | 2024 |
| Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset FH O'Shea, S Joung, D Smith, D Ratner, RN Coffee Machine Learning: Science and Technology, 2024 | | 2024 |
| Real-time plasma confinement mode classification with deep neural networks and high-bandwidth edge fluctuation measurements in DIII-D K Gill, D Smith, S Joung, B Geiger, G McKee, J Zimmerman, R Coffee, ... APS Division of Plasma Physics Meeting Abstracts 2023, UP11. 098, 2023 | | 2023 |
| Deep neural network based real-time ELM prediction and reconstruction of turbulent flow based on the DIII-D BES measurement S Joung, D Smith, B Geiger, K Gill, G McKee, Z Yan, J Zimmerman, ... APS Division of Plasma Physics Meeting Abstracts 2023, NP11. 028, 2023 | | 2023 |
| Real-time confinement mode classification and ELM onset prediction with the BES diagnostic system at DIII-D S Joung, D Smith, B Geiger, K Gill, G McKee, Z Yan, J Zimmerman, ... APS Division of Plasma Physics Meeting Abstracts 2022, UP11. 085, 2022 | | 2022 |
| Inference of spatially continuous kinetic profiles with Gaussian processes and neural networks in KSTAR M KIM, S JOUNG, WH KO, JH LEE, YC GHIM 2021 KPS Spring Meeting, 2021 | | 2021 |
| Learning plasma equilibria from scratch with deep neural network Grad-Shafranov solver S Joung, J Kim, S Kwak, M Kim, HS Kim, JG Bak, YC Ghim APS Division of Plasma Physics Meeting Abstracts 2021, PO07. 003, 2021 | | 2021 |
| Predicting plasma pressure profiles with Gaussian process and a neural network in KSTAR based on magnetic signals M Kim, S Joung, WH Ko, JH Lee, YC Ghim APS Division of Plasma Physics Meeting Abstracts 2021, TP11. 110, 2021 | | 2021 |
