Multi-Output Surrogate Construction for Fusion Simulations

Computational simulation has allowed scientists to explore, observe, and test physical regimes previously thought to be unattainable. Bayesian analysis provides a natural framework for incorporating the uncertainties that undeniably exist in computational modeling. In the absence of a reliable low-fidelity physics model, phenomenological surrogate models can be used to mitigate the expense of performing Bayesian analysis and uncertainty quantification; however, phenomenological models may not adhere to known physics or properties. Furthermore, the interactions of complex physics in high-fidelity codes lead to dependencies between quantities of interest (QoIs) that are difficult to quantify and capture when individual surrogates are used for each observable. 

 

We present a method of constructing GPs that emulate multiple QoIs simultaneously. As an exemplar, we consider Magnetized Linear Inertial Fusion, a fusion concept that relies on the direct compression of magnetized, laser-heated fuel by a metal liner to achieve thermonuclear ignition. The calibration of diagnostic metrics is complicated by sparse experimental data and expensive high-fidelity neutron transport models. The use of a surrogate is therefore warranted, the development of which raises long-standing issues in modeling and simulation, including calibration, validation, and uncertainty quantification.