Modeling of electron nonlocal transport in plasmas using artificial neural networks.

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.

High performance modelling of the transport of energetic particles for photon radiotherapy.

This work consists of the validation of a new Grid Based Boltzmann Solver (GBBS) conceived for the description of the transport and energy deposition by energetic particles for radiotherapy purposes. The entropic closure and a compact mathematical formulation allow our code (M1) to calculate the delivered dose with an accuracy comparable to the Monte-Carlo (MC) codes with a computational time that is reduced to the order of few minutes without any special processing power requirement. A validation protocol with heterogeneity inserts has been defined for different photon sources. The comparison with the MC calculated depth-dose curves and transverse profiles of the beam at different depths shows an excellent accuracy of the M1 model.