Goran Arbanas
Nuclear Data - Research and Development Staff
Bio
Developing theory and codes of nuclear cross sections as well as their uncertainties for nuclear data evaluations of thermal neutron scattering, resolved as well as unresolved resonances regions, optical potentials, and direct-semidirect neutron capture cross sections. Developing a Bayesian formalism for imperfect models or data for the next generation of uncertainty quantification (UQ) of differential cross sections and integral benchmarks. Exploring applications of Bayesian Machine Learning with UQ for scientific discovery from nuclear data.
Publications
June 2023
Journal: Nuclear Science and Engineering
April 2023
Journal: Annals of Nuclear Energy
February 2023
Journal: Annals of Nuclear Energy