Joint Diffusion Approach to Multimodal Inference in Inertial Confinement Fusion

A combination of physics-based simulation and experiments has been critical to achieving ignition in inertial confinement fusion (ICF). Simulation and experiment both produce a mixture of scalar and image outputs; however, only a subset of simulated data are available experimentally. We introduce a generative framework, called JointDiff, which enables predictions of conditional simulation input and output distributions from partial, multimodal observations. The model leverages joint diffusion to unify forward surrogate modeling, inverse inference, and output imputation into one architecture. We train our model on a large ensemble of three-dimensional multirocket piston simulations and demonstrate high accuracy, statistical robustness, and promising transfer to experiments performed at the National Ignition Facility, with further improvements achieved through experimental fine-tuning. This work establishes JointDiff as a flexible generative surrogate for multimodal scientific tasks, with implications for understanding diagnostic constraints, aligning simulation to experiment, and accelerating ICF design.