Coupling Computationally Expensive Radiative Hydrodynamic Simulations with Machine Learning for Graded Inner Shell Design Optimization in Double Shell Capsules

High energy density experiments rely heavily on predictive physics simulations in the design process. Specifically in inertial confinement fusion (ICF), predictive physics simulations, such as in the radiation-hydrodynamics code xRAGE, are computationally expensive, limiting the design process and ability to find an optimal design. Machine learning provides a mechanism to leverage expensive simulation data and alleviate limitations on computational time and resources in the search for an optimal design. Machine learning efficiently identifies regions of design space with high predicted performance as well as regions with high uncertainty to focus simulations, which may lead to unexpected designs with great potential. This dissertation focuses on the application of Bayesian optimization to design optimization for ICF experiments conducted by the double shell campaign at Los Alamos National Lab (LANL). The double shell campaign is interested in implementing graded inner shell layers to their capsule geometry. Graded inner shell layers are expected to improve stability in the implosions with fewer sharp density jumps, but at the cost of lower yields, in comparison to the nominal bilayer inner shell targets. This work explores minimizing hydrodynamic instability and maximizing yield for the graded inner shell targets by building and coupling a multi-fidelity Bayesian optimization framework with multi-dimensional xRAGE simulations for an improved design process.