5 papers
Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration
Henry Shaowu Yuchi, Michael Grosskopf, Aman Sharma +4
Gaussian processes are widely used for surrogate modeling in computer experiments, which often produce numerous intermediate variables that are not explicitly used in standard cali…
Microscopic theory of angular momentum distributions across the full range of fission fragments
Petar MareviÄ, Nicolas Schunck, Marc Verriere
Modern nuclear theory provides qualitative insights into the fundamental mechanisms of nuclear fission and is increasingly capable of making reliable quantitative predictions. Most…
Learning nuclear cross sections across the chart of nuclides with graph neural networks
Hongjun Choi, Sinjini Mitra, Jason Brodksy +6
In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus…
Excitation energy of fission fragments within nuclear time-dependent density functional theory
Antonio BjelÄiÄ, Nicolas Schunck, Marc Verriere
The number and properties of the neutrons and photons emitted in nuclear fission are directly related to the excitation energy of the fission fragments when they are formed at scis…
Bayesian model mixing with multi-reference energy density functional
Aman Sharma, Nicolas Schunck, Kyle Wendt
Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technolo…