| Direct comparison between Bayesian and frequentist uncertainty quantification for nuclear reactions GB King, AE Lovell, L Neufcourt, FM Nunes Physical review letters 122 (23), 232502, 2019 | 83 | 2019 |
| Fission fragment decay simulations with the CGMF code P Talou, I Stetcu, P Jaffke, ME Rising, AE Lovell, T Kawano Computer Physics Communications 269, 108087, 2021 | 56 | 2021 |
| Measurement of the prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons of energy 1–20 MeV KJ Kelly, M Devlin, JM O'Donnell, JA Gomez, D Neudecker, RC Haight, ... Physical Review C 102 (3), 034615, 2020 | 44 | 2020 |
| Uncertainty quantification for optical model parameters AE Lovell, FM Nunes, J Sarich, SM Wild Physical Review C 95 (2), 024611, 2017 | 41 | 2017 |
| Constraining transfer cross sections using Bayes' theorem AE Lovell, FM Nunes Physical Review C 97 (6), 064612, 2018 | 38 | 2018 |
| Toward emulating nuclear reactions using eigenvector continuation C Drischler, M Quinonez, PG Giuliani, AE Lovell, FM Nunes Physics Letters B 823, 136777, 2021 | 37 | 2021 |
| Systematic uncertainties in direct reaction theories AE Lovell, FM Nunes Journal of Physics G: nuclear and particle physics 42 (3), 034014, 2015 | 36 | 2015 |
| Preequilibrium Asymmetries in the Prompt Fission Neutron Spectrum KJ Kelly, T Kawano, JM O’Donnell, JA Gomez, M Devlin, D Neudecker, ... Physical Review Letters 122 (7), 072503, 2019 | 33 | 2019 |
| Nuclear masses learned from a probabilistic neural network AE Lovell, AT Mohan, TM Sprouse, MR Mumpower Physical Review C 106 (1), 014305, 2022 | 31 | 2022 |
| Extension of the Hauser-Feshbach fission fragment decay model to multichance fission AE Lovell, T Kawano, S Okumura, I Stetcu, MR Mumpower, P Talou Physical Review C 103 (1), 014615, 2021 | 31 | 2021 |
| Quantifying uncertainties on fission fragment mass yields with mixture density networks AE Lovell, AT Mohan, P Talou Journal of Physics G: Nuclear and Particle Physics 47 (11), 114001, 2020 | 29 | 2020 |
| Recent advances in the quantification of uncertainties in reaction theory AE Lovell, FM Nunes, M Catacora-Rios, GB King Journal of Physics G: Nuclear and Particle Physics 48 (1), 014001, 2020 | 27 | 2020 |
| Physically interpretable machine learning for nuclear masses MR Mumpower, TM Sprouse, AE Lovell, AT Mohan Physical Review C 106 (2), L021301, 2022 | 26 | 2022 |
| Exploring experimental conditions to reduce uncertainties in the optical potential M Catacora-Rios, GB King, AE Lovell, FM Nunes Physical Review C 100 (6), 064615, 2019 | 25 | 2019 |
| Informing nuclear physics via machine learning methods with differential and integral experiments D Neudecker, O Cabellos, AR Clark, MJ Grosskopf, W Haeck, ... Physical Review C 104 (3), 034611, 2021 | 24 | 2021 |
| Energy dependence of nonlocal optical potentials AE Lovell, PL Bacq, P Capel, FM Nunes, LJ Titus Physical Review C 96 (5), 051601, 2017 | 24 | 2017 |
| Three-body model for the two-neutron emission of AE Lovell, FM Nunes, IJ Thompson Physical Review C 95 (3), 034605, 2017 | 24 | 2017 |
| Angular Momentum Removal by Neutron and -Ray Emissions during Fission Fragment Decays I Stetcu, AE Lovell, P Talou, T Kawano, S Marin, SA Pozzi, A Bulgac Physical Review Letters 127 (22), 222502, 2021 | 23 | 2021 |
| Uncertainty quantification due to optical potentials in models for () reactions GB King, AE Lovell, FM Nunes Physical Review C 98 (4), 044623, 2018 | 23 | 2018 |
| Exploration of the energy dependence of proton nonlocal optical potentials MI Jaghoub, AE Lovell, FM Nunes Physical Review C 98 (2), 024609, 2018 | 23 | 2018 |
