  A real-time capable core turbulence tokamak transport model is developed.
This model is constructed from the regularized nonlinear regression of
quasilinear gyrokinetic transport code output. The regression is performed with
a multilayer perceptron neural network. The transport code input for the neural
network training set consists of five dimensions, and is limited to adiabatic
electrons. The neural network model successfully reproduces transport fluxes
predicted by the original quasilinear model, while gaining five orders of
magnitude in computation time. The model is implemented in a real-time capable
tokamak simulator, and simulates a 300s ITER discharge in 10s. This
proof-of-principle for regression based transport models anticipates a
significant widening of input space dimensionality and physics realism for
future training sets. This aims to provide unprecedented computational speed
coupled with first-principle based physics for real-time control and integrated
modelling applications.
