99 citations · 177 across the 7 of their papers we have counts for
9 papers
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…
First-principles-based multiple-isotope particle transport modelling at JET
M. Marin, J. Citrin, C. Bourdelle +6
Core turbulent particle transport with multiple isotopes can display observable differences in behaviour between the electron and ion particle channels. Experimental observations a…
Multiple-isotope pellet cycles captured by turbulent transport modelling in the JET tokamak
M. Marin, J. Citrin, L. Garzotti +8
For the first time the pellet cycle of a multiple-isotope plasma is successfully reproduced with reduced turbulent transport modelling, within an integrated simulation framework. F…
Spatially resolved determination of the electronic density and temperature by a visible spectrotomography diagnostic in a linear magnetized plasma
V. Gonzalez-Fernandez, P. David, R. Baude +2
In this work, a non-intrusive, spatially resolved, spectro-tomographic optical diagnostic of the electronic density and temperature on the linear plasma column Mistral is presented…
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…
Global scaling of the heat transport in fusion plasmas
Sara Moradi, Johan Anderson, Michele Romanelli +2
A global heat flux model based on a fractional derivative of plasma pressure is proposed for the heat transport in fusion plasmas. The degree of the fractional derivative of the he…