Fast modeling of turbulent transport in fusion plasmas using neural networks

This repository contains the source code of the publication "Fast modeling of turbulent transport in fusion plasmas using neural networks" by K.L. van de Plassche et al., submitted to Physics of Plasmas. Currently contains only the pre-print version. On publication, a reproduction package to reproduce all figures in this manuscript will be supplied.

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 million flux calculations of the quasilinear gyroki-
netic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical
features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural
network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid
control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through
application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent trans-
port of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately
reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours
are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between
QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with
differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup
following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak
modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quan-
tification, and control applications.