This course contains the use of artificial intelligence.
Explore the Future of Nuclear Energy, Artificial Intelligence, and Intelligent Power Systems
What happens when two of the world’s most transformative technologies—Nuclear Engineering and Artificial Intelligence (AI)—come together?
Welcome to Nuclear Engineering and AI, a comprehensive, hands-on course exploring how Machine Learning, Deep Learning, Predictive Analytics, and Digital Twin Technology are transforming nuclear energy systems while nuclear power emerges as a potential energy source for AI Data Centers and high-performance computing infrastructure.
As electricity demand from Generative AI, Large Language Models (LLMs), and GPU-powered computing grows, reliable, low-carbon electricity becomes increasingly important. Meanwhile, nuclear facilities are exploring AI-Powered Monitoring, Predictive Maintenance, Engineering Simulations, and intelligent decision-support technologies.
This course connects both worlds, combining nuclear engineering fundamentals with practical AI applications.
What Will You Learn?
Throughout this course, you’ll explore:
-
Nuclear Engineering Fundamentals: Nuclear fission, reactor physics, thermodynamics, heat transfer, radiation safety, and electricity generation.
-
Artificial Intelligence in Nuclear Energy: Machine learning algorithms, neural networks, deep learning concepts, and engineering data analytics.
-
Digital Twins and Simulation: Develop simplified digital twins, physics-based simulations, and AI-assisted engineering models.
-
Predictive Maintenance and Anomaly Detection: Analyze equipment telemetry, predict degradation, and identify abnormal operating patterns.
-
Nuclear Fusion and Plasma Research: Explore tokamaks, magnetic confinement, plasma behavior, and AI applications in fusion research.
-
Small Modular Reactors (SMRs): Understand advanced nuclear reactors, microreactors, modular energy systems, and emerging deployment models.
-
Nuclear-Powered AI Data Centers: Evaluate energy requirements for GPU clusters, cloud computing, and AI infrastructure.
-
Cybersecurity and AI Governance: Understand nuclear cybersecurity, responsible AI, explainable AI (XAI), regulatory considerations, and safety assurance.
-
Generative AI for Engineering: Explore LLMs, Retrieval-Augmented Generation (RAG), and AI-assisted technical knowledge management.
Learn Through Practical Projects
This isn’t just a theoretical nuclear engineering course.
You’ll work with Python, Jupyter Notebook, Pandas, NumPy, scikit-learn, and engineering visualization tools to develop practical prototypes using simulated datasets.
Hands-on exercises include building Machine Learning Models, equipment monitoring dashboards, Predictive Maintenance Systems, anomaly detection algorithms, simplified Digital Twins, and AI data center electricity demand models.
You’ll also explore the opportunities and limitations of applying Artificial Intelligence to Safety-Critical Systems, including model validation, uncertainty, human oversight, and regulatory expectations.
Final Capstone: Aurora Nuclear and AI Energy Campus
Bring everything together by designing a conceptual AI-Enabled Nuclear Energy Campus.
Evaluate nuclear electricity supply options, estimate AI data center energy demand, create monitoring prototypes, develop digital twin simulations, and propose an engineering architecture incorporating AI Security, Nuclear Safety, and Responsible AI Governance.
All technical exercises use educational simulations rather than operational nuclear systems.
Who Should Enroll?
This course is designed for Nuclear Engineers, AI Engineers, Machine Learning Developers, Data Scientists, Energy Professionals, Electrical Engineers, Data Center Architects, Cybersecurity Specialists, technology leaders, researchers, and students interested in the intersection of Nuclear Technology, Artificial Intelligence, and Clean Energy Innovation.
No previous nuclear engineering experience is required.
Join us to explore how AI can support nuclear engineering—and how nuclear energy could power the next generation of AI infrastructure.
