Multimodal prediction of tearing instabilities in a tokamak
Tokamak is a torus-shaped nuclear fusion device that uses magnetic fields to confine fusion
fuel in the form of plasma. Tearing instability in plasma is a major issue in which the …
fuel in the form of plasma. Tearing instability in plasma is a major issue in which the …
Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO2 interferometer on DIII-D
Abstract A Machine-Learning (ML) based detection scheme that automatically detects Alfvén
Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is …
Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is …
Experimental identification of ion cyclotron emission on HL-2A using YOLO neural network algorithm
Identification of magnetohydrodynamics (MHD) instabilities with neural networks has been
extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron …
extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron …
Segmentation of MHD modes using Fourier transform, wavelets and computer vision algorithms
Magnetohydrodynamic (MHD) activity in fusion devices is typically analyzed by examining
time-frequency spectrograms obtained from various diagnostics. MHD modes often co-exist …
time-frequency spectrograms obtained from various diagnostics. MHD modes often co-exist …
Alfvén eigenmode detection using Long-Short Term Memory Networks and CO2 Interferometer data on the DIII-D National Fusion Facility
The successful steady-state operation of burning fusion plasmas in planned future devices
such as the ITER tokamak requires understanding of fast-ion physics. Alfven eigenmodes …
such as the ITER tokamak requires understanding of fast-ion physics. Alfven eigenmodes …
A novel unsupervised machine learning algorithm for automatic Alfvénic activity detection in the TJ-II stellarator
A novel sparse encoding algorithm is developed to detect and study plasma instabilities
automatically. This algorithm, called Elastic Random Mode Decomposition, is applied to the …
automatically. This algorithm, called Elastic Random Mode Decomposition, is applied to the …
Prediction of Fishbone Linear Instability in Tokamaks with Machine Learning Methods
ZY Liu, HR Qiu, GY Fu, Y **ao, YC Chen… - arxiv preprint arxiv …, 2024 - arxiv.org
A machine learning based surrogate model for fishbone linear instability in tokamaks is
constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K …
constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K …
Identification of MHD modes on EAST using a deep learning framework
L Kong, B Guo, B Shen, T Shi, D Chen… - Plasma Physics and …, 2023 - iopscience.iop.org
The improvement of plasma parameters is severely limited by magnetohydrodynamic (MHD)
instabilities. The identification of MHD modes is crucial for the study and control of MHD …
instabilities. The identification of MHD modes is crucial for the study and control of MHD …
Application of neural networks in beam emission spectroscopy modelling
Beam emission spectroscopy (BES) is an active plasma diagnostic utilized for plasma
density measurements. BES synthetic diagnostics are computationally expensive and …
density measurements. BES synthetic diagnostics are computationally expensive and …
A novel unsupervised machine learning algorithm for automatic Alfvénic activity detection in the TJ-II stellarator
A novel sparse encoding algorithm is developed to detect and study plasma instabilities
automatically. This algorithm, called Elastic Random Mode Decomposition, is applied to the …
automatically. This algorithm, called Elastic Random Mode Decomposition, is applied to the …