Sobes’ research covers a broad spectrum of reactor physics. Research interests currently span four major areas from nuclear physics to reactor design. With a background in nuclear data, he continues to work on problems in the nuclear data pipeline with a particular interest in the application of Artificial Intelligence (AI)/ Machine Learning (ML) algorithms.
For an up-to-date list of publications and professional activities please see Sobes' CV here.
Further research projects look at how nuclear data enters radiation transport calculations and how reformulating the nuclear data representation can accelerate simulations of nuclear systems on modern heterogeneous computing architectures (HPC). Research in Sensitivity/Uncertainty (S/U) analysis methods continues to be of interest. Application of S/U methods to nuclear systems design coupled with AI/ML methods is a new area of research. Last but not least, the entire research portfolio is applied to the design and future operation of the Fast Neutron Source experimental facility proposed to be built on the UT campus.
Massachusetts Institute of Technology, PhD Nuclear Science and Engineering, Feb. 2011–Sept. 2013
Thesis: Coupled Differential and Integral Data Analysis for Improved Uncertainty Quantification of the Cu-63,65 Cross Section Evaluations.
Massachusetts Institute of Technology, BS Nuclear Science and Engineering, Sept. 2007–Feb. 2011
Thesis: Individual Pebble Temperature Peaking Factor due to Local Pebble Arrangement in a Pebble Bed Reactor Core.