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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21006 |
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| _version_ | 1866911420398960640 |
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| author | Jones, Michael S. Kunimune, Justin Casey, Daniel Kustowski, Bogdan Kur, Eugene Humbird, Kelli |
| author_facet | Jones, Michael S. Kunimune, Justin Casey, Daniel Kustowski, Bogdan Kur, Eugene Humbird, Kelli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A joint diffusion approach to multi-modal inference in inertial confinement fusion Jones, Michael S. Kunimune, Justin Casey, Daniel Kustowski, Bogdan Kur, Eugene Humbird, Kelli Plasma Physics 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. |
| title | A joint diffusion approach to multi-modal inference in inertial confinement fusion |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2601.21006 |