Goran Arbanas

R&D Staff

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.