Conference Agenda

This paper explores how artificial intelligence (AI)—particularly multimodal foundation models (FM), intelligent digital twins (IDT), and LLM-based multi-agent systems—is reshaping nuclear thermal-hydraulics and safety analysis (NTHSA). Historically grounded in physics-based modeling, structured validation, and expert-guided reasoning, NTHSA now faces growing demands for more adaptive, predictive, and transparent methodologies. Emerging AI technologies offer the potential to augment these foundations, enabling a shift from static safety analysis to a dynamic, epistemically intelligent safety paradigm.

The paper introduces foundation models—large-scale AI systems trained on diverse textual, numerical, and visual data—as tools that can reason, generalize, and automate complex tasks such as PIRT generation, closure model selection, and physics-code scripting. When embedded within intelligent digital twins, AI can enable real-time plant monitoring, anomaly diagnosis, and adaptive margin management, all grounded in both operational data and physics-based simulations. The integration of multi-agent architectures further allows the decomposition of safety analysis workflows into autonomous, collaborative AI roles—streamlining V&V, optimizing test matrices, and ensuring traceable, auditable recommendations.

This AI-enhanced framework not only accelerates traditional EMDAP loops but also opens the door to earning back conservatism through evidence-based learning. By dynamically reducing epistemic uncertainty over time, safety margins can be optimized while maintaining robust defense-in-depth. Case studies—such as the NAMAC framework and GPT-based discrepancy checkers—illustrate how AI can act as an assistant or advisor, improving explainability, trust, and operational awareness.

The paper also highlights critical challenges: limited nuclear data, explainability of black-box models, online V&V, cybersecurity, and human factors. It advocates for incremental adoption—starting with pilot deployments in non-critical systems, and expanding under transparent, auditable, and regulator-engaged oversight. Emphasizing ethics, human-AI collaboration, and sociotechnical integration, the paper charts a path toward AI as a trusted partner in nuclear safety.

Ultimately, AI is not portrayed as a silver bullet but as a transformative augmentation to the safety toolkit—empowering engineers and regulators to maintain high standards of performance and safety in an increasingly complex operational landscape.

The rapid advancement of artificial intelligence (AI) is reshaping global energy demand, notably increasing the need for stable, carbon-free power sources. As AI-driven services and data centers expand, major economies are revisiting nuclear power as a reliable energy solution. This paper analyzes recent discussions from NURETH-18 through 20, highlighting AI’s emerging role in nuclear thermal-hydraulics and system diagnostics. It further examines global nuclear expansion trends in response to projected electricity demand growth and decarbonization goals. The development of small modular reactors (SMRs), with enhanced safety and modular construction, is accelerating worldwide. Concurrently, regulatory frameworks are evolving to accommodate advanced reactor technologies, as exemplified by the U.S. 10 CFR Part 53 initiative. AI applications in nuclear operations, including anomaly detection, predictive maintenance, and documentation analysis, offer opportunities for efficiency and safety gains but raise new challenges in verification, regulation, and accountability. This paper addresses both technical and regulatory challenges for developers and regulators in adopting AI and deploying next-generation reactors. It concludes that while AI is driving power demand, it also holds potential to support nuclear innovation—provided appropriate safety, governance, and validation mechanisms are established.

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