Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive

Abstract

Three machine learning techniques (multilayer perceptron, random forest and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modelling and real-time control applications. The machine learning models use a database of more than 16 000 GENRAY/CQL3D simulations for training, validation and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters ($n_{e0}$,$T_{e0}$,$I_p$,$B_t$,$R_0$,$n_{\|}$,$Z_{{\rm eff}}$,$V_{{\rm loop}}$and$P_{{\rm LHCD}}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to$\sim$ms with high accuracy across the input parameter space.