References
- K. IKEDA, “ITER on the Road to Fusion Energy,” Nucl. Fusion, 50, 014002 (2009); https://doi.org/10.1088/0029-5515/50/1/014002.
- A. CREELY et al., “Overview of the SPARC Tokamak,” J. Plasma Phys., 86 (2020); https://doi.org/10.1017/S0022377820001257.
- D. HUMPHREYS et al., “Advanced Tokamak Operation Using the DIII–D Plasma Control System,” Fusion Eng. Des., 66–68, 663 (2003); https://linkinghub.elsevier.com/retrieve/pii/S0920379603003223.
- E. J. STRAIT et al., “Progress in Disruption Prevention for ITER,” Nucl. Fusion, 59, 112012 (2019); https://doi.org/10.1088/1741-4326/ab15de.
- M. L. WALKER et al., “Assessment of Controllers and Scenario Control Performance for ITER First Plasma,” Fusion Eng. Des., 146, 1853 (2019); https://doi.org/10.1016/j.fusengdes.2019.03.050.
- P. DE VRIES, M. JOHNSON, and I. SEGUI, “Statistical Analysis of Disruptions in JET,” Nucl. Fusion, 49, 055011 (2009); https://doi.org/10.1088/0029-5515/49/5/055011.
- B. CANNAS et al., “Automatic Disruption Classification at JET: Comparison of Different Pattern Recognition Techniques,” Nucl. Fusion, 46, 7, 699 (2006); https://doi.org/10.1088/0029-5515/46/7/002.
- G. RATTÁ et al., “An Advanced Disruption Predictor for JET Tested in a Simulated Real-Time Environment,” Nucl. Fusion, 50, 2, 025005 (2010); https://doi.org/10.1088/0029-5515/50/2/025005.
- J. W. BERKERY et al., “A Reduced Resistive Wall Mode Kinetic Stability Model for Disruption Forecasting,” Phys. Plasmas, 24, 5, 056103 (2017); https://doi.org/10.1063/1.4977464.
- A. PAU et al., “A Machine Learning Approach Based on Generative Topographic Mapping for Disruption Prevention and Avoidance at JET,” Nucl. Fusion, 59, 10, 106017 (2019); https://doi.org/10.1088/1741-4326/ab2ea9.
- J. VEGA et al., “Results of the JET Real-Time Disruption Predictor in the ITER-Like Wall Campaigns,” Fusion Eng. Des., 88, 6–8, 1228 (2013); https://doi.org/10.1016/j.fusengdes.2013.03.003.
- K. J. MONTES et al., “Machine Learning for Disruption Warnings on Alcator C-Mod, DIII-D, and EAST,” Nucl. Fusion, 59, 096015 (2019); https://doi.org/10.1088/1741-4326/ab1df4.
- C. REA et al., “A Real-Time Machine Learning-Based Disruption Predictor in DIII-D,” Nucl. Fusion, 59, 9, 096016 (2019); https://doi.org/10.1088/1741-4326/ab28bf.
- D. R. FERREIRA, P. J. CARVALHO, and H. FERNANDES, “Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data,” IEEE Trans. Plasma Sci., 48, 1, 36 (2020); https://doi.org/10.1109/TPS.2019.2947304.
- J. KATES-HARBECK, A. SVYATKOVSKIY, and W. TANG, “Predicting Disruptive Instabilities in Controlled Fusion Plasmas Through Deep Learning,” Nature, 568, 7753, 526 (2019); https://doi.org/10.1038/s41586-019-1116-4.
- G. MONTAVON, W. SAMEK, and K. R. MÜLLER, “Methods for Interpreting and Understanding Deep Neural Networks,” Digital Signal Process., 73, 1 (2018); https://doi.org/10.1016/j.dsp.2017.10.011.
- M. SUNDARARAJAN, A. TALY, and Q. YAN, “Axiomatic Attribution for Deep Networks,” presented at the 34th Int. Conf. on Machine Learning (ICML 2017), Sydney, Australia, August 6–11, 2017, 70 (2017); http://proceedings.mlr.press/v70/sundararajan17a.html.
- A. PAU et al., “A First Analysis of JET Plasma Profile-Based Indicators for Disruption Prediction and Avoidance,” IEEE Trans. Plasma Sci., 46, 7, 2691 (2018); https://doi.org/10.1109/TPS.2018.2841394.
- P. DE VRIES et al., “Scaling of the MHD Perturbation Amplitude Required to Trigger a Disruption and Predictions for ITER,” Nucl. Fusion, 56, 2, 026007 (2016); https://doi.org/10.1088/0029-5515/56/2/026007.
- R. SWEENEY et al., “Statistical Analysis of m/n = 2/1 Locked and Quasi-Stationary Modes with Rotating Precursors at DIII-D,” Nucl. Fusion, 57, 016019 (2017); https://doi.org/10.1088/0029-5515/57/1/016019.
- C. SOZZI et al., “Early Identification of Disruption Paths for Prevention and Avoidance,” resented at the 27th IAEA Fusion Energy Conf. (IAEA CN-258), Gandhinagar, India, October 22–27, 2018; https://conferences.iaea.org/event/151/contributions/6273/.
- A. W. LEONARD et al., “2D Tomography with Bolometry in DIII-D,” Rev. Sci. Instrum., 66, 2, 1201 (1995); https://doi.org/10.1063/1.1146006.
- B. LIPSCHULTZ, “Review of MARFE Phenomena in Tokamaks,” J. Nucl. Mater, 145–147, 15 (1987); https://doi.org/10.1016/0022-3115(87)90306-0.
- D. M. PONCE-MARQUEZ et al., “Thomson Scattering Diagnostic Upgrade on DIII-D,” Rev. Sci. Instrum., 81, 10, 10D525 (2010); https://doi.org/10.1063/1.3495759.
- C. REA and R. S. GRANETZ, “Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D,” Fusion Sci. Technol., 74, 1–2, 89 (2018); https://doi.org/10.1080/15361055.2017.1407206.
- E. ALESSI et al., “Analysis of Rotating MHD Perturbations to Identify Disruptive Phases in TCV Tokamak,” presented at the 46th EPS Conference on Plasma Physics, Milan, Italy, July 8–12, 2019; http://ocs.ciemat.es/EPS2019PAP/pdf/P4.1058.pdf.
- P. DE VRIES et al., “Survey of Disruption Causes at JET,” Nucl. Fusion, 51, 5, 053018 (2011); https://doi.org/10.1088/0029-5515/51/5/053018.
- P. C. DE VRIES et al., “The Impact of the ITER-Like Wall at JET on Disruptions,” Plasma Phys. Control. Fusion, 54, 12, 124032 (2012); https://doi.org/10.1088/0741-3335/54/12/124032.
- X. D. DU et al., “Direct Measurements of Internal Structures of Born-Locked Modes and the Key Role in Triggering Tokamak Disruptions,” Phys. Plasmas, 26, 4, 042505 (2019); https://doi.org/10.1063/1.5085329.
- C. REA et al., “Disruption Prevention via Interpretable Data-Driven Algorithms on DIII-D and EAST,” IAEA FEC 2020, International Atomic Energy Agency (2020).
- N. T. VU et al., “Tokamak-Agnostic Actuator Management for Multi-Task Integrated Control with Application to TCV and ITER,” Fusion Eng. Des., 147, 111260 (2019); https://doi.org/10.1016/j.fusengdes.2019.111260.
- O. MENEGHINI et al., “Integrated Modeling Applications for Tokamak Experiments with OMFIT,” Nucl. Fusion, 55, 083008 (2015); https://doi.org/10.1088/0029-5515/55/8/083008.