Meta-Learned Bayesian Optimization for Energy Yield in Inertial Confinement Fusion

With the growing demand for clean energy, fusion presents a promising path to sustainable power generation. Inertial Confinement Fusion (ICF) experiments trigger nuclear reactions by firing lasers at a target. These experiments are costly and require complex optimization of the laser pulse shape across multiple shots to maximize yield. Even though Bayesian Optimization (BO) has been commonly used to optimize such expensive scientific experiments, vanilla BO methods do not leverage prior knowledge of the function from simulations or past experiments and fail to achieve high sample efficiency. In this work, we adapted and explored BO meta-learning techniques for ICF that either meta-learn the BO surrogate model, the acquisition function, or both from simulations. Our results demonstrate that three meta-learning techniques, Meta-Learning Acquisition Functions for BO (MetaBO), Rank-Weighted Gaussian Process Ensemble (RGPE), and Neural Acquisition Processes (NAP), drastically reduce the number of experiments needed to achieve a satisfactory yield.