Optimizing the Performance of Direct-Drive Implosion Experiments Using Meta-Bayesian Optimization

Finding a laser pulse shape that optimizes the Lawson parameter [1,2,3] for a given target is a challenging problem in inertial confinement fusion due to the predictive gap between simulations and experiments. The Lawson parameter is typically related to the yield and ρR of the implosion and requires an increase in both. Optimizing the yield of cryogenic implosions on OMEGA using a data-driven predictive machine-learning (ML) approach [4,5] has met with considerable success, but increasing the ρR has proven more challenging. It is likely that this is in part due to hydrodynamic instabilities, but is likely also due to the increased sensitivity of the ρR to fine details of the shock timing and entropy spatial profile of the implosion, which in turn are highly sensitive to the front end of the laser pulse. If simulations used for implosion design [6] fail to capture the instability growth, shock transit, or adiabat-setting behavior of the implosion correctly, the response surface between simulations and experiments will sharply differ, making implosion optimization challenging with limited experimental data. We present the use of Neural Acquisition Processes (NAP) [7] which is meta-learned on varying fidelities of simulation databases to optimize synthetic experiments and Omega experiments in a sample efficient manner. NAP uses a transformer neural network to learn the input-output distribution and enables proximal policy optimization reinforcement learning based acquisition function which significantly outperforms Bayesian optimization. Additionally, we present algorithmic improvements of meta-learning variants of decision transformers [8] and use them to solve similar ICF implosion optimization objectives.