Towards a Multi-Modal Foundation Model for Inertial Confinement Fusion: Combining Structured Data and Diagnostic Images

Michael Jones, Bogdan Kustowski

Keywords: Foundation model, structured data, multi-modal, inertial confinement fusion, diffusion models

TL;DR: We introduce a diffusion-based model that jointly learns structured and image inertial confinement fusion (ICF) data, leveraging simulation pretraining to overcome experimental data scarcity and predict multi-modal conditional distributions.

Abstract: Inertial confinement fusion (ICF) offers a pathway to sustainable energy production, but achieving controlled fusion requires precise modeling of complex structured and image data. Recent breakthroughs underscore the need for scalable methods to analyze multi-modal diagnostic data and simulations, which include scalar inputs, scalar outputs, and image outputs. In this work, we present a diffusion-based generative framework designed to model the joint and conditional distributions of these structured and image data. By leveraging simulation data for pretraining, our approach addresses the challenge of experimental data scarcity and enables robust conditional modeling tasks. This work represents a prototype towards an ICF foundation model, and its architecture is transferable to diverse multi-modal scientific problems.

Submission Number: 51