An application of survival analysis to disruption prediction via Random Forests

Tinguely, R. A.; Montes, Kevin; Rea, Cristina; Sweeney, R.; Granetz, R.

doi:10.1088/1361-6587/ab32fc

Cited by 27 publications

(24 citation statements)

References 40 publications

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“…The lack of comprehensive first-principle models has led researchers to develop data-driven solutions to predict the occurrence of disruptions in existing tokamaks: current efforts cover most if not all experiments (still in operation or shut down); for a comprehensive list of references on disruption prediction literature, the reader is directed to references 3–28 in Tinguely et al. (2019). Nevertheless, very little work has been done to extrapolate predictions to yet-to-be-built devices.…”

Section: Disruption Statistics Mitigation and Predictionmentioning

confidence: 99%

MHD stability and disruptions in the SPARC tokamak

et al. 2020

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SPARC is being designed to operate with a normalized beta of $\beta _N=1.0$ , a normalized density of $n_G=0.37$ and a safety factor of $q_{95}\approx 3.4$ , providing a comfortable margin to their respective disruption limits. Further, a low beta poloidal $\beta _p=0.19$ at the safety factor $q=2$ surface reduces the drive for neoclassical tearing modes, which together with a frozen-in classically stable current profile might allow access to a robustly tearing-free operating space. Although the inherent stability is expected to reduce the frequency of disruptions, the disruption loading is comparable to and in some cases higher than that of ITER. The machine is being designed to withstand the predicted unmitigated axisymmetric halo current forces up to 50 MN and similarly large loads from eddy currents forced to flow poloidally in the vacuum vessel. Runaway electron (RE) simulations using GO+CODE show high flattop-to-RE current conversions in the absence of seed losses, although NIMROD modelling predicts losses of ${\sim }80$ %; self-consistent modelling is ongoing. A passive RE mitigation coil designed to drive stochastic RE losses is being considered and COMSOL modelling predicts peak normalized fields at the plasma of order $10^{-2}$ that rises linearly with a change in the plasma current. Massive material injection is planned to reduce the disruption loading. A data-driven approach to predict an oncoming disruption and trigger mitigation is discussed.

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Section: Disruption Statistics Mitigation and Predictionmentioning

confidence: 99%

MHD stability and disruptions in the SPARC tokamak

et al. 2020

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“…The RF algorithm has been applied to the prediction of disruption is a variety of tokamaks such as DIII-D [423], JET [424], Alcator C-Mod [425], and EAST [426], and it has been successfully integrated with the real-time plasma control system on DIII-D and EAST. Disruptivity, that is the final probability of disruption, is characterized by the average result of decision trees to classify disruption/non-disruption from training.…”

Section: E Prediction Of Tokamak Disruptionmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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“…A real-time machine learning used for disruption prediction in DIII-D was put forward, and a clear path toward the design of disruption avoidance strategies was provided in 2019 [8] . A speeding calculation based on the neural network is used to explore the parameter scope of dominant instabilities, considering the global effects in tokamak plasmas [9] . An application of the combination of an existing disruption prediction machine learning algorithm and the Kaplan-Meier estimator of survival probability is reported concerning three Alcator C-Mod plasma discharges [10] .…”

Section: Development and Application Of Machine Learning In Magnetic ...mentioning

confidence: 99%