Fine-tuning a Multi-modal Generative Model for Inertial Confinement Fusion on NIF Experiments

Inertial confinement fusion (ICF) experiments are expensive and sparse, while the simulations used to model them often exhibit discrepancies with experimental observations. Moreover, experiments provide only partial and noisy measurements across a heterogeneous suite of diagnostics, making adaptation of simulation-trained models challenging. We address this problem by fine-tuning a multi-modal generative diffusion model, pretrained on ICF simulations, using data from 48 National Ignition Facility (NIF) experiments. Our framework combines (1) imputation of missing experimental modalities, (2) simulation replay to maintain supervision over unobserved quantities, and (3) a round-trip objective that encourages self-consistency across inference directions. We demonstrate improved prediction of scalar quantities and neutron images on held-out experiments, as well as enhanced consistency under round-trip inference tests. Our method enables transfer of simulation-learned multi-modal representations to heterogeneous and incomplete experimental observations, and is extensible to other physical domains.