NuclearDiffusion: Text-to-Image Foundation Models for Learning Nuclear Energy Concepts
Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored. As an exmaple in nuclear engineering, general-purpose foundation models frequently generate physically incorrect or conceptually inconsistent images because they lack domain-specific knowledge. This work presents one of the first systematic studies of domain adaptation for nuclear text-to-image generation through fine-tuning of open-source diffusion models. We curate a dataset of 1,000 captioned nuclear energy images spanning reactors, fuel cycles, radiation, and related concepts, and use it to fine-tune three state-of-th...
| Authors | Mohammed I. Radaideh, Jeremy Moon, Andre Gala-Garza et al. |
|---|---|
| Primary category | cs.GR |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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