Bayesian Methods in Inertial Confinement Fusion Research: Embracing uncertainty and Learning More from our Data*

Bayesian methods have recently been applied to a wide range of problems in the physical sciences, allowing physicists extract more information from the available data with quantified uncertainties.  Statistical inference is at the heart of many critical tasks such as feature extraction, hypothesis testing, and parameter estimation.  Here we will briefly review the formalism that underlies these tasks before describing the novel ways in which Bayesian methods are being applied in the field of inertial confinement fusion.  These applications include data assimilation, the process of combining multiple disparate measurements to constrain a model, optimization of diagnostic configurations to minimize uncertainty, and automated feature extraction.  When combined with deep-learning enabled surrogate models, these tools are able to efficiently capture complex physics with high fidelity, furthering our understanding in ways that were previously impossible.  *SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525