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As Artificial Intelligence (AI) becomes increasingly integrated into the physical world, its application within nuclear safety–critical domains has come under heightened scrutiny. While AI has demonstrated considerable effectiveness in perception-based tasks—such as processing data, images, patterns, and speech—its deployment in nuclear safety applications presents significant challenges. In particular, the data-driven and often opaque “black box” nature of conventional AI systems limits their ability to provide transparent, auditable rationales for their outputs. This lack of explainability raises regulatory and industry concerns, as it may introduce unknown or unacceptable safety risks if AI-generated recommendations are implemented without sufficient understanding or validation.

To address these challenges, an emerging field of research is focused on the development of Explainable and Physics-Informed Artificial Intelligence. In this approach, AI models are explicitly constrained and guided by the Laws of Physics, system dynamics, and governing equations embedded directly within the neural network architecture. This paradigm supports the development of trustworthy AI systems that are capable of delivering accurate, rapid, robust, and interpretable solutions while remaining consistent with established physical principles.

This presentation will describe the methodology and challenges associated with training AI systems to recognise, respect, and enforce physical laws when formulating predictions and decisions. It will demonstrate how Physics-Informed AI can transform radiation protection by ensuring that AI outputs remain physically plausible and scientifically defensible, thereby improving accuracy, reliability, and interpretability in complex and safety-critical environments. Finally, emerging applications across nuclear and environmental safety, as well as medical physics, will be discussed, highlighting the potential of this approach to support regulatory acceptance and operational deployment.

Presented by Dr Ahmed Aslam (Amentum)

Ahmed is a nuclear physicist and leads the Physics and Digital Technologies Team at Amentum. He has more than 30 years of experience delivering and leading major programmes in nuclear physics and digital technologies, with a particular focus on the convergence of the two domains.

In addition to his industry role, Ahmed is a Lecturer in Nuclear Technology at the University of Manchester, where he contributes to the education and development of the next generation of nuclear professionals.

Ahmed is also a member of the Steering Board for the Nuclear Institute’s AI4Nuclear programme, where the remit includes supporting the nuclear industry in the adoption of novel AI technologies, aligning sector activities with the UK National AI Strategy, and addressing the technical, regulatory, and cultural challenges associated with deploying AI within the nuclear sector.