Ryan McClarren

Associate Professor, Aerospace and Mechanical Engineering

Friday, May 5
2:30-3:00 p.m.

“Simulation and Machine Learning for Fusion and Fission Energy Systems”


In this talk I will cover some of the work ongoing at Notre Dame regarding the radiative transfer and neutron transport for fusion and fission energy systems. Topics also include a discussion of the current state of inertial fusion energy and new technologies in fission energy systems. Along the way we will see what are the open research questions and opportunities for collaboration across disciplines.


Prof. Ryan McClarren joined the College of Engineering at the University of Notre Dame as an associate professor in the Department of Aerospace and Mechanical Engineering in 2017. He is also involved with Buzzer Intelligence, a startup driven to improve the event going experience. Previously, he was Assistant Professor of Nuclear Engineering at Texas A&M University, as well as the Chief Science officer for Farsite. He received his Ph.D., M.S.E., and B.S.E. from the University of Michigan. 

His research interests include particle transport (neutrons, x-rays, and friends), uncertainty quantification, and high energy density physics. This includes modeling and simulation of a variety of energy systems with particular emphasis on particle transport calculations related to neutron behavior in fission systems and radiative transfer in inertial confinement fusion experiments. The McClarren group works on developing algorithms that perform on the world's most powerful supercomputers, building technologies for quantifying the uncertainties in simulation-based predictions, and using machine learning to enhance simulations. He has published over 45 articles in refereed journals and given many presentations at national and international conferences. 

Relevant Energy Publications
  1. McClarren, Ryan G., and Cory D. Hauck. "Robust and accurate filtered spherical harmonics expansions for radiative transfer." Journal of Computational Physics 229, no. 16 (2010): 5597-5614.
  2. McClarren, Ryan G. Uncertainty quantification and predictive computational science. Springer International Publishing, 2018.
  3. McClarren, Ryan G. Computational nuclear engineering and radiological science using python. Academic Press, 2017.
  4. McClarren, Ryan G. "Calculating time eigenvalues of the neutron transport equation with dynamic mode decomposition." Nuclear Science and Engineering 193, no. 8 (2019): 854-867.
  5. Bennett, William, and Ryan G. McClarren. "Self-similar solutions for high-energy density radiative transfer with separate ion and electron temperatures." Proceedings of the Royal Society A 477, no. 2249 (2021): 20210119.

Department Website
Group Website