Neural-Network accelerated fusion transport simulations for ITER scenario modeling

Preparation for ITER operation relies on our ability to efficiently predict the plasma confinement with high physics fidelity. High-fidelity turbulent transport models such as TGLF remain one of the major bottlenecks in this process. To accelerate prediction of the turbulent transport, the neural network (NN) approach of TGLF (TGLF-NN) described in [Meneghini NF 2017] has been generalized to support predictions for ITER both during its commissioning phase (H only) and nuclear phase (D+T and He ash). In this talk we describe the techniques used to sample the 20+ dimensions input parameters space and assemble a simulation database that is suitable for training robust machine learning based models. Tuning of the NN model hyper-parameters was carried out on GPU enabled clusters, both using Gaussian process based optimization, and random sampling of the configuration space. Dimensional reduction with auto-encoders, and training on a latent-space data-set was also investigated. Results of coupled core-pedestal ITER simulations leveraging the latest TGLF-NN model will be presented.