  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 gyrokinetic
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 transport 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 Quantification, and control applications.
