3 citations · 6 across the 7 of their papers we have counts for
7 papers
Quantum Complexity in Nuclear Scattering and Fission Dynamics
Saurabh V. Kadam, Antonio Bjelčić, Nicolas Schunck +1
Quantum computers promise advantages for simulating strongly correlated quantum many-body systems, like atomic nuclei, that are beyond the reach of classical computers. Realizing t…
Nuclear Reaction Data for Fission Products Off Stability
Gustavo Nobre, Emanuel Chimanski, Aman Sharma +4
Neutron cross sections on fission products are relevant to a wide range of applications, including nuclear nonproliferation and forensics, spent-fuel assay, reactor burnup and desi…
Quantifying uncertainty in machine learning on nuclear binding energy
Mengyao Huang, Kyle A. Wendt, Nicolas F. Schunck +1
Techniques from artificial intelligence and machine learning are increasingly employed in nuclear theory, however, the uncertainties that arise from the complex parameter manifold…
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…
Evaluation of phase shifts for non-relativistic elastic scattering using quantum computers
Francesco Turro, Kyle A. Wendt, Sofia Quaglioni +2
Simulations of scattering processes are essential in understanding the physics of our universe. Computing relevant scattering quantities from ab initio methods is extremely difficu…
Illuminating Systematic Trends in Nuclear Data with Generative Machine Learning Models
Jordan M. R. Fox, Kyle A. Wendt
We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit in…
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…