Nuclear renaissance Dean Price: AI tools for advanced reactors

Dean Price, assistant professor in MIT's Department of Nuclear Science and Engineering, outlined a roadmap on April 3, 2026 for applying artificial intelligence across the nuclear industry to accelerate design, licensing and operations of small modular and advanced reactors. He described specific AI methods, data needs and regulatory engagement steps intended to cut engineering cycles and improve safety margins.

How Price proposes to use AI

Price identified several technical threads where AI can change routine tasks into automated workflows. He emphasized digital twins that combine high-fidelity physics simulation with machine-learned surrogate models to run many-more design variations at far lower computational cost. That approach pairs model-order reduction and uncertainty quantification so surrogate models retain safety-relevant bounds while enabling rapid parametric optimization.

Design automation is a major focus: Price argues that coupling generative design tools with multi-physics simulation could reduce early-stage engineering iteration for reactor cores, heat-exchanger layouts and balance-of-plant systems. He highlighted machine learning for materials screening and for predicting component degradation from sensor streams, enabling predictive maintenance schedules rather than fixed intervals.

On licensing, Price said AI can help produce the probabilistic risk assessments and sensitivity studies regulators expect, by automating scenario generation and running many more Monte Carlo cases via fast surrogates. He called for standardized data formats and open test suites so models used in licensing can be audited and compared.

Price also discussed operational AI: real-time anomaly detection using physics-aware models, digital-twin-driven operator decision support, and the integration of sensor networks with edge inference to maintain situational awareness during transient events. He urged investment in testbeds where new algorithms can be exercised under realistic control-room conditions.

Barriers and next steps

Price acknowledged significant hurdles. Nuclear datasets are fragmented across vendors, utilities and research institutions, and privacy and commercial concerns limit sharing. He recommended curated, de-identified datasets and regulatory sandboxes where data and models can be evaluated without exposing proprietary systems.

Model interpretability and validation were another concern. Price stressed that black-box models will win little regulatory confidence unless paired with rigorous uncertainty bounds and physics constraints. He recommended hybrid modeling approaches that embed conservation laws into machine-learning pipelines and deliver explainable outputs for engineers and regulators.

Cybersecurity and supply-chain resilience must be addressed, he said, since connected digital twins and remote inference expand attack surfaces. Price proposed certification pathways for AI components analogous to those used for conventional reactor software, and closer collaboration between the nuclear industry, regulators and AI researchers to develop testing standards.

Price called for workforce investment: training nuclear engineers in data science and machine-learning practitioners in reactor physics. He sketched collaborations between universities, national labs and industry to build shared tools and validation environments.

Why it matters

If Price's roadmap is followed, AI could shorten design cycles and lower costs for small modular and advanced reactors by automating routine engineering and expanding the scope of safety analyses. That shift would affect reactor vendors, regulators and utilities, making technical standards, data sharing and model validation the next battlegrounds for nuclear innovation.