NSR Query Results
Output year order : Descending NSR database version of April 27, 2024. Search: Author = M.J.Grosskopf Found 4 matches. 2021NE06 Phys.Rev. C 104, 034611 (2021) D.Neudecker, O.Cabellos, A.R.Clark, M.J.Grosskopf, W.Haeck, M.W.Herman, J.Hutchinson, T.Kawano, A.E.Lovell, I.Stetcu, P.Talou, S.Vander Wiel Informing nuclear physics via machine learning methods with differential and integral experiments NUCLEAR REACTIONS 238U(n, n'), E=14 MeV; analyzed pulsed-sphere neutron-leakage experimental spectrum obtained at LLNL facility, and compared with evaluated data in ENDF/B-VIII.0. 241Pu(n, F), E=0.1-1.0, 14 MeV; analyzed differential and integral experimental data for fission σ(E) by combining experimental σ data, nuclear-physics theory and neutron-transport simulations of the experiments using machine learning (ML) random forest algorithm and expert judgment. Relevance to improvement of description of nuclear-physics observables in particular application areas.
doi: 10.1103/PhysRevC.104.034611
2020NE07 Nucl.Data Sheets 167, 36 (2020) D.Neudecker, M.Grosskopf, M.Herman, W.Haeck, P.Grechanuk, S.Vander Wiel, M.E.Rising, A.C.Kahler, N.Sly, P.Talou Enhancing nuclear data validation analysis by using machine learning
doi: 10.1016/j.nds.2020.07.002
2020SC08 J.Phys.(London) G47, 074001 (2020) N.Schunck, J.O'Neal, M.Grosskopf, E.Lawrence, S.M.Wild Calibration of energy density functionals with deformed nuclei
doi: 10.1088/1361-6471/ab8745
2020WH03 Nucl.Instrum.Methods Phys.Res. A978, 164305 (2020) B.Whewell, M.Grosskopf, D.Neudecker Evaluating 239Pu(n, f) cross sections via machine learning using experimental data, covariances, and measurement features NUCLEAR REACTIONS 239Pu(n, F), E<20 MeV; analyzed available data; deduced evaluated σ using a modified version of the Hybrid Robust Support Vector Machine and logistic regression with elastic net regularization and random forest regression with SHAP feature importance metric.
doi: 10.1016/j.nima.2020.164305
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