Optimizing Cylindrical Targets for Neutron Yield Using Multi-Fidelity Modeling Techniques

Design studies of inertial confinement fusion targets can present computational challenges, as they often require many calls to multi-dimensional radiation-hydrodynamics codes to iterate on a design and accurately capture implosion physics. Surrogate models, which replace simulation results with a simplified approximation, have been successfully leveraged in the past to reduce the computational expense of such design studies. We apply Gaussian process based surrogate models coupled with Bayesian optimization to optimize the design of a cylindrical target containing deuterium-tritium fuel for yield. Past work, which focused on the optimization of Gaussian process surrogates trained exclusively on output from 1D simulations, revealed that optimal designs selected in this manner exhibited a substantial loss in yield when simulated in 2D. Despite their lower prediction accuracy, 1D simulations are less expensive than their 2D counterparts. To improve the predictive performance of the surrogate while maintaining low costs, we introduce a multifidelity optimization algorithm that integrates data from 1D and 2D simulations to identify target designs that maximize yield. We compare the costs and predictive accuracy of the multifidelity optimization method with those of an optimization approach which solely relies on high-fidelity data. The design selected by the algorithm is discussed, emphasizing the design choices and implosion physics responsible for the target's improved performance.