COLLEGE STATION, September 21 — Texas A&M University brought together leaders from academia, national laboratories, industry and government last month for a two-day summit on how artificial intelligence and machine learning are reshaping nuclear engineering — and how to move those capabilities from the laboratory into trusted technology.
The AI for Nuclear Summit, held August 19–20, was hosted by the Texas A&M Department of Nuclear Engineering in collaboration with The Texas A&M University System Nuclear Security Office and the Texas A&M Institute of Data Science, according to a report by contributor Julianne Hodges dated September 19. The program centered on three interconnected areas: foundational AI, applied nuclear AI and industry deployment.
Its stated objective was to connect AI advances with the specialized scientific, engineering and regulatory requirements of nuclear technologies. Speakers included faculty from Texas A&M and other universities, representatives from nuclear companies, and scientists from several U.S. Department of Energy national laboratories.
Bringing physics and safety culture to the table
Dr. Yang Liu, summit organizer and assistant professor of nuclear engineering, described the collaboration as a meeting of distinct strengths. AI researchers bring methods from foundation models to agentic systems, he said, while nuclear engineers bring the physics, the data and a safety culture that has to be respected. National laboratories bring facilities, mission problems and computing power, Liu added.
Regulators decide what counts as credible evidence, and industry knows what it takes to deploy a reactor or build a data center. Dr. Ayman Hawari, professor and head of the nuclear engineering department, said artificial intelligence is moving faster than any tool the field has adopted before and that no single institution can chart the path alone.
The summit was convened, he said, so the nuclear community — from national laboratories and industry to the classroom — can shape that path together.
Where AI meets the physics of the field
The first day opened with discussions on foundational AI, the importance of data, and the use of AI and machine learning across scientific fields. The first keynote speaker was Dr. Karthik Duraisamy, director of the Michigan Institute for Computational Discovery and Engineering at the University of Michigan. His presentation set the stage for discussions about integrating AI with physics-based science rather than treating it as a replacement for traditional modeling and simulation.
Speakers went on to discuss AI for research in nuclear physics, fusion energy, materials used in nuclear reactors, building advanced reactors, and unique data challenges in nuclear security and nonproliferation. Liu pointed to a persistent obstacle in the field.
Nuclear engineering has a lack of data, he said, because experiments can be expensive and high-fidelity simulations are time-consuming. He believes AI can help ease that problem. A first-day topic was how AI must follow physics and how research should explore gaps in data, uncertainty and validation.
Liu said surrogate models learned from simulations can predict in seconds what used to take hours, allowing exploration of thousands of design options for advanced reactors instead of a handful. Physics-informed machine learning, he said, helps get more out of sparse experimental data, speeds discovery of materials that can survive inside a reactor, and supports control of fusion plasmas.
From research to regulation and deployment
The second day shifted to deployment and regulation of AI, security, industry adoption and collaboration among academia, industry and government. Presentations came from Texas A&M faculty and representatives from Argonne National Laboratory, Pacific Northwest National Laboratory and Savannah River National Laboratory. Keynote speaker Christopher Ritter, division director of scientific computing and AI at Idaho National Laboratory, spoke about Prometheus, an initiative that is part of the DOE’s Genesis Mission to accelerate deployment of nuclear energy.
Ritter explained how these initiatives will guide the United States to use AI for shortening the design, licensing and construction cycle for new reactors while preserving safety and reliability. A later panel examined the Genesis Mission and how universities and government can work together to develop the research and workforce needed to move AI-enabled nuclear technologies forward.
It brought together Texas A&M faculty with representatives from Argonne, Sandia National Laboratory and Los Alamos National Laboratory. Representatives from NVIDIA, Oklo, the U.S. Regulatory Commission, Idaho National Laboratory and Texas A&M examined regulation, industry deployment and the growing relationship between computing and energy infrastructure. Liu said that if the implementation is done right, the impact is measured in years cut from deployment timelines and lower costs, while preserving the industry’s safety record.
Students get a seat at the table
Texas A&M students shared their research during a poster session each day, discussing their work directly with scientists, engineers and industry leaders. Zaid Abulawi, a graduate student in nuclear engineering, said presenting his work to scientists from the national laboratories and hearing their questions in real time was the best feedback he could ask for and showed him where his research stands.
What to watch next is whether the partnerships forged at the summit translate into the workforce and research pipelines the Genesis Mission will need — and whether the students who stood beside their posters become the engineers who carry AI-enabled reactor design from simulation into construction.
