LAUSR

This article presents the use of artificial neural networks (ANN) to predict nonlocal heat flux transport within hydrodynamic simulations. Several cases of laser driven ablation of a plastic target are considered. The database for the ANN training phase is built using the transport module of the hydrodynamic code CHIC. It covers a range of parameters characteristic of laser experiments in the context of high-energy-density physics. Results show that an ANN can efficiently replace a module of nonlocal transport in one- and two-dimensional hydrodynamic simulations, with an error less than 3% in a radius of 0.5μm and an average computation gain of a factor 433 in two dimensions.