A joint diffusion approach to multi-modal inference in inertial confinement fusion

TL;DR

JointDiff is a novel generative framework that unifies forward modeling, inverse inference, and data imputation for multi-modal inertial confinement fusion data, improving predictive accuracy and transferability.

Contribution

The paper introduces JointDiff, a joint diffusion-based model that handles multi-modal ICF data, enabling conditional predictions and data imputation from partial observations.

Findings

High accuracy in predicting simulation and experimental data

Robustness and transferability demonstrated on NIF experiments

Flexible generative surrogate for multi-modal scientific tasks

Abstract

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 images 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, multi-modal 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 Multi-Rocket Piston simulations and demonstrate high accuracy, statistical robustness, and transferability to experiments performed at the National Ignition Facility (NIF). This work establishes JointDiff as a flexible generative surrogate for multi-modal scientific tasks, with implications for understanding diagnostic constraints, aligning simulation to experiment, and accelerating ICF design.