Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction
Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one million dollars, making accurate AI surrogates a high-value target. We study exogenous-driven ICF waveform prediction, where a 512-step neutron-rate diagnostic must be inferred directly from a laser pulse and target design parameters, with no historical response observed. The regime stresses standard time-series predictors with temporal sparsity (picosecond peak in a nanosecond window), input-output scale mismatch (under 300 real shots), and peak sensitivity (picosecond timing). We propose ICF-DLM, to our knowledge the first LM-based ICF pre...
| Authors | Xiang Zhang, Varchas Gopalaswamy, Rahman Ejaz et al. |
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| Primary category | cs.LG |
| Published | 7 September 2026 |
| Last updated at source | 7 October 2026 |
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