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physics.plasm-ph2021
Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling
Aaron Ho, Jonathan Citrin, Fulvio Auriemma +7
This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques w…
physics.plasm-ph2021
Neural network surrogate of QuaLiKiz using JET experimental data to populate training space
Aaron Ho, Jonathan Citrin, Clarisse Bourdelle +5
Within integrated tokamak plasma modelling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge analysis. Neur…
physics.plasm-ph2019
Fast modeling of turbulent transport in fusion plasmas using neural networks
Karel Lucas van de Plassche, Jonathan Citrin, Clarisse Bourdelle +7
We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 mill…