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 …

Overview of the SPARC physics basis towards the exploration of burning-plasma regimes in high-field, compact tokamaks

P Rodriguez-Fernandez, AJ Creely… - Nuclear …, 2022 - iopscience.iop.org
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 …

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

A Murari, R Rossi, T Craciunescu, J Vega… - Nature …, 2024 - nature.com
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 …

Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches

AA Kaptanoglu, KD Morgan, CJ Hansen, SL Brunton - Physical Review E, 2021 - APS
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 …

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 …

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 …

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 …

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 …

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 …

[HTML][HTML] Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal …

RM Churchill, B Tobias, Y Zhu, DIII-D team - Physics of Plasmas, 2020 - pubs.aip.org
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 …