arXiv:2606.05202v1 Announce Type: cross Abstract: In reactor physics, neutronics can be treated with different fidelity levels, according to the needs of the user. On one hand, the precise modeling of neutrons' behaviour in reactor physics is often expensive and time-consuming due to the high computational costs to numerically solve the Boltzmann transport equation. Conversely, by adopting suitable assumptions, such as the SP$_N$, diffusion theory, and point kinetics, it is possible to generate efficiently low-fidelity data. From the perspective of surrogate models, this computational limitati
The increasing computational demands of detailed physics simulations, coupled with advancements in AI/ML, are driving the development of more efficient surrogate models.
Improving the efficiency of reactor physics simulations can accelerate nuclear energy research, development, and deployment, impacting energy independence and climate goals.
The ability to generate accurate high-fidelity results with significantly reduced computational cost via multi-fidelity AI models could change the pace of reactor design and optimization.
- · Nuclear energy sector
- · AI/ML research in scientific computing
- · National laboratories
- · Traditional high-performance computing providers for niche physics simulations w
Faster and cheaper development cycles for new nuclear reactor designs and fuel cycles.
Increased investment and interest in nuclear fusion and advanced fission, potentially accelerating their commercial viability.
Enhanced energy security and a more diversified global energy mix as nuclear power becomes more accessible and cost-effective due to accelerated innovation.
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