Yang Liu
Texas A&M University
Department of Nuclear Engineering
Biography
Dr. Yang Liu is an Assistant Professor of Nuclear Engineering at Texas A&M University. He leads the Scientific Machine learning for Advanced Reactor Technologies (SMART) lab and serves as the director of the Generative AI for Science and Engineering (GAISE) Lab under Texas A&M Institute of Data Science. Prior to joining TAMU, he held positions as staff at Argonne and postdoc at University of Michigan. His research focuses on the intersection between AI/ML and advanced energy systems, including (a). Physics-informed machine learning; (b). Digital twin-enhanced experimentation; and (c). Generative AI for science and engineering. Dr. Liu has published more than 50 papers in refereed journals and conference proceedings. He is a recipient of US. Department of Energy Nuclear Energy Office's Distinguished Early Career Award in 2024.
SRP Project Title
LLM-Enhanced Cyber-Physical Testbed for Advanced Reactor Monitoring and Predictive Maintenance
NAIRR Project
LLM-Enhanced Cyber-Physical Testbed for Advanced Reactor Monitoring and Predictive Maintenance
Topical Areas
Artificial Intelligence and Intelligent Systems; Mechanical Engineering; Other Engineering and Technologies
Abstract
This project aims to harness cutting-edge large language models (LLMs) to enhance real-time monitoring, anomaly detection, and predictive maintenance in a thermal-fluid facility designed for advanced nuclear reactors. By integrating sensor data, experimental logs, and domain-specific knowledge, the LLM-based platform will identify off-normal events and potential cybersecurity threats, providing timely diagnostics to operators. The resulting methods and tools will be published openly, enabling broader adoption within the nuclear industry and fostering safer, more efficient reactor operations.
Desired Skills
Generative AI, Large language model, Retrieval-Augmented-Generation, AI Agents, Context engineering.
Lightning Talk Title
Domain Knowledge-Enhanced Generative AI for Advanced Energy Research and Development
Keywords
Generative AI; Advanced Energy Systems; Automating Engineering Workflow; Real-Time Remote Monitoring and Control; Modeling and Simulation; Retrieval Augmented Generation