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Disruption prediction with artificial intelligence techniques in tokamak plasmas
J Vega, A Murari, S Dormido-Canto, GA Rattá… - Nature Physics, 2022 - nature.com
In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100
million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape …
million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape …
Overview of the SPARC physics basis towards the exploration of burning-plasma regimes in high-field, compact tokamaks
The SPARC tokamak project, currently in engineering design, aims to achieve breakeven
and burning plasma conditions in a compact device, thanks to new developments in high …
and burning plasma conditions in a compact device, thanks to new developments in high …
A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors
The objective of thermonuclear fusion consists of producing electricity from the coalescence
of light nuclei in high temperature plasmas. The most promising route to fusion envisages …
of light nuclei in high temperature plasmas. The most promising route to fusion envisages …
Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches
Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to
understand and describe their behavior. However, there is a scarcity of plasma models of …
understand and describe their behavior. However, there is a scarcity of plasma models of …
Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
LL Lao, S Kruger, C Akçay… - Plasma Physics and …, 2022 - iopscience.iop.org
Recent progress in the application of machine learning (ML)/artificial intelligence (AI)
algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for …
algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for …
A real-time machine learning-based disruption predictor in DIII-D
C Rea, KJ Montes, KG Erickson, RS Granetz… - Nuclear …, 2019 - iopscience.iop.org
A disruption prediction algorithm, called disruption prediction using random forests (DPRF),
has run in real-time in the DIII-D plasma control system (PCS) for more than 900 discharges …
has run in real-time in the DIII-D plasma control system (PCS) for more than 900 discharges …
Disruption prediction on EAST tokamak using a deep learning algorithm
BH Guo, DL Chen, B Shen, C Rea… - Plasma Physics and …, 2021 - iopscience.iop.org
In this study, a long short-term memory (LSTM) model is trained on a large disruption
warning database to predict the disruption on EAST tokomak. To compare the performance …
warning database to predict the disruption on EAST tokomak. To compare the performance …
Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles
E Aymerich, G Sias, F Pisano, B Cannas… - Nuclear …, 2022 - iopscience.iop.org
In view of the future high power nuclear fusion experiments, the early identification of
disruptions is a mandatory requirement, and presently the main goal is moving from the …
disruptions is a mandatory requirement, and presently the main goal is moving from the …
All superconducting tokamak: EAST
J Hu, W **, J Zhang, L Huang, D Yao, Q Zang, Y Hu… - AAPPS Bulletin, 2023 - Springer
Abstract Experimental Advanced Superconducting Tokamak (EAST) was built to
demonstrate high-power, long-pulse operations under fusion-relevant conditions, with major …
demonstrate high-power, long-pulse operations under fusion-relevant conditions, with major …
[HTML][HTML] Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal …
In this paper, we discuss recent advances in deep convolutional neural networks (CNNs) for
sequence learning, which allow identifying long-range, multi-scale phenomena in long …
sequence learning, which allow identifying long-range, multi-scale phenomena in long …