Physically Interpretable Machine Learning for nuclear masses
M. R. Mumpower · T. M. Sprouse · A. E. Lovell · A. T. Mohan
Mar 22, 2022#2nucl-thPRC67 citations
Author
Sprouse, Trevor M.
INSPIRE profile1817465ORCID0000-0002-4375-4369
6 papers·1,043 citations in this corpus
Beta-delayed fission in the coupled Quasi-particle Random Phase Approximation plus Hauser-Feshbach approach
M. R. Mumpower · T. Kawano · T. M. Sprouse
Jan 11, 2022#3nucl-thPRC15 citations
Nuclear masses learned from a probabilistic neural network
A.E. Lovell · A.T. Mohan · T.M. Sprouse · M.R. Mumpower
Jan 4, 2022#5nucl-thPRC60 citations
Following nuclei through nucleosynthesis: a novel tracing technique
T. M. Sprouse · M. R. Mumpower · R. Surman
Aug 17, 2020#1nucl-thPRC38 citations
Propagation of Statistical Uncertainties of Skyrme Mass Models to Simulations of -Process Nucleosynthesis
T. M. Sprouse · R. Navarro Perez · R. Surman · M. R. Mumpower · G. C. McLaughlin · N. Schunck
Jan 30, 2019#5nucl-thPRC51 citations
Estimation of M1 scissors mode strength for deformed nuclei in the medium to heavy mass region by statistical Hauser-Feshbach model calculations
M. R. Mumpower · T. Kawano · J. L. Ullmann · M. Krtička · T. M. Sprouse
Jun 26, 2017#2nucl-thPRC54 citations
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