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Plasma surrogate modelling using Fourier neural operators
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of
sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma …
sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma …
Data-driven models in fusion exhaust: AI methods and perspectives
A review is given on the highlights of a scatter-shot approach of develo** machine-
learning methods and artificial neural networks based fast predictors for the application to …
learning methods and artificial neural networks based fast predictors for the application to …
Equivariant neural simulators for stochastic spatiotemporal dynamics
Neural networks are emerging as a tool for scalable data-driven simulation of high-
dimensional dynamical systems, especially in settings where numerical methods are …
dimensional dynamical systems, especially in settings where numerical methods are …
Data efficiency and long term prediction capabilities for neural operator surrogate models of core and edge plasma codes
Simulation-based plasma scenario development, optimization and control are crucial
elements towards the successful deployment of next-generation experimental tokamaks and …
elements towards the successful deployment of next-generation experimental tokamaks and …
Multi-machine benchmark of the self-consistent 1D scrape-off layer model DIV1D from stagnation point to target with SOLPS-ITER
This paper extends a 1D dynamic physics-based model of the scrape-off layer (SOL)
plasma, DIV1D, to include the core SOL and possibly a second target. The extended model …
plasma, DIV1D, to include the core SOL and possibly a second target. The extended model …
A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media
Fourier neural operators (FNOs) are invariant with respect to the size of input images, and
thus images with any size can be fed into FNO-based frameworks without any modification …
thus images with any size can be fed into FNO-based frameworks without any modification …
NTVTOK-ML: Fast surrogate model for neoclassical toroidal viscosity torque calculation in tokamaks based on machine learning methods
XT Yan, NN Bao, CY Zhao, YW Sun, YT Meng… - Computer Physics …, 2025 - Elsevier
Abstract The Neoclassical Toroidal Viscosity (NTV) torque is a crucial source of toroidal
momentum in tokamaks, exerting significant influence on plasma instability and …
momentum in tokamaks, exerting significant influence on plasma instability and …
Prediction of fishbone linear instability in tokamaks with machine learning methods
ZY Liu, HR Qiu, GY Fu, Y **ao, YC Chen… - Nuclear …, 2024 - iopscience.iop.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 …
[HTML][HTML] Detachment scalings derived from 1D scrape-off-layer simulations
Fusion power plants will require detachment to mitigate sputtering and keep divertor heat
fluxes at tolerable levels. Controlling detachment on these devices may require the use of …
fluxes at tolerable levels. Controlling detachment on these devices may require the use of …
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Simulation is a powerful tool to better understand physical systems, but generally requires
computationally expensive numerical methods. Downstream applications of such …
computationally expensive numerical methods. Downstream applications of such …