The JAG inertial confinement fusion simulation dataset for multi-modal scientific deep learning. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative

The goal of this project is to build better surrogate models for Inertial Confinement Fusion (ICF) using neural networks. Particularly, we are interested in replicating the behavior of the JAG 1D Semi-analytic simulator for ICF.

The JAG model has been designed to give a rapid description of the observables from ICF experiments, which are all generated very late in the implosion. In this way the very complex and computationally expensive transport models needed to describe the capsule drive can be avoided, allowing a single solution in $ ilde$ seconds. The trade-off is that JAG inputs do not relate to actual experimental observables, rather the state of the implosion once the laser drive has switched off. At that point, an analytic description of the spatial profile inside the hotspot can be found [1,2], leaving only a set of coupled ODEs describing the temporal energy balance inside the entire problem which can be solved easily [3]. The various terms in the energy balance equation relate to different physics processes (radiation, electron conduction, heating by alpha particles, etc), making JAG useful for investigating the role of various potentially uncertain physics models. Combined with a thin-shell model describing the 3D hydrodynamic evolution of the hotspot [4], JAG has a detailed description of the spatial and temporal evolution of all thermodynamic variables which can be post-process to predict a full range of experimental observables.

References:
1. Betti et al., Physics of Plasmas 9, 2277 (2002)
2. Springer et al., EPJ Web. Conferences 59:04001 (2013)
3. Betti et al., Physical Review Letters 114:255003 (2015)
4. Ott et al., Physical Review letters 29:1429 (1995)