Data-driven Prediction of Scaling and Ignition of Inertial Confinement Fusion Experiments

Recent advances in inertial confinement fusion (ICF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. High-fidelity radiation-hydrodynamics simulations are used to specify target and laser parameters, interpret data, and to explore novel designs for future experiments. These tasks are approached by a combination of post-shot analysis, in which simulations are calibrated to existing NIF data through a range of tuning parameters, and pre-shot simulation where the calibrated model is applied to future experiments. Quantifying the uncertainty in post-shot and pre-shot analyses is critical in measuring the state of our understanding of given experimental platform, assigning confidence to predictions for a new design, and reliably comparing physics hypotheses; this is, however, a significant challenge because NIF experiments are sparse, incompletely diagnosed, and subject to unknown random shot-to-shot variations.

We have developed a data-driven approach to uncertainty quantification for post-shot and pre-shot analysis that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive statistical model for performance parameters that is jointly informed by data from multiple NIF shots and the simulations. The prediction distribution captures experimental uncertainty, expert priors, design changes and shot-to-shot variations to provide a new capability to make uncertain performance predictions for experimental designs before they are performed at NIF.



In this talk we will discuss our approach and demonstrate how including data from both simulation and experiment results in a better constrained and more physical prediction. We will then describe the application to a recent ignition (gain=1.5) experiment, for which our pre-shot approach predicted a significantly higher probability of ignition compared to previous top performers (gain=0.7). Finally, we will describe future directions for this work including the use of data-driven models to help design robust high-yield platforms at NIF and beyond, and perspectives for other topics in high-energy-density and plasma physics.