| In gas laser ionization and spectroscopy experiments at the Superconducting Separator Spectrometer (S3): Conceptual studies and preliminary design R Ferrer, B Bastin, D Boilley, P Creemers, P Delahaye, E Liénard, ... Nuclear Instruments and Methods in Physics Research Section B: Beam …, 2013 | 70 | 2013 |
| Machine learning the nuclear mass ZP Gao, YJ Wang, HL Lü, QF Li, CW Shen, L Liu Nuclear Science and Techniques 32 (10), 109, 2021 | 64 | 2021 |
| Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies F Li, Y Wang, H Lü, P Li, Q Li, F Liu Journal of Physics G: Nuclear and Particle Physics 47 (11), 115104, 2020 | 35 | 2020 |
| Fusion hindrance of heavy ions: role of the neck D Boilley, H Lü, C Shen, Y Abe, BG Giraud Physical Review C 84 (5), 054608, 2011 | 34 | 2011 |
| Synthesis of superheavy elements: Uncertainty analysis to improve the predictive power of reaction models H Lü, D Boilley, Y Abe, C Shen Physical Review C 94 (3), 034616, 2016 | 31 | 2016 |
| Approximate Bayesian computation via the energy statistic HD Nguyen, J Arbel, H Lü, F Forbes IEEE Access 8, 131683-131698, 2020 | 29 | 2020 |
| Application of machine learning in the determination of impact parameter in the system F Li, Y Wang, Z Gao, P Li, H Lü, Q Li, CY Tsang, MB Tsang Physical Review C 104 (3), 034608, 2021 | 24 | 2021 |
| KEWPIE2: A cascade code for the study of dynamical decay of excited nuclei H Lü, A Marchix, Y Abe, D Boilley Computer Physics Communications 200, 381-399, 2016 | 22 | 2016 |
| Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning Y Wang, F Li, Q Li, H Lü, K Zhou Physics Letters B 822, 136669, 2021 | 17 | 2021 |
| Uncertainty analysis of the nuclear liquid drop model B Cauchois, H Lü, D Boilley, G Royer Physical Review C 98 (2), 024305, 2018 | 11 | 2018 |
| Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning Y Wang, Z Gao, H Lü, Q Li Physics Letters B 835, 137508, 2022 | 8 | 2022 |
| Bayesian nonparametric priors for hidden Markov random fields H Lü, J Arbel, F Forbes Statistics and Computing 30 (4), 1015-1035, 2020 | 7 | 2020 |
| How accurately can we predict synthesis cross sections of superheavy elements? D Boilley, B Cauchois, H Lü, A Marchix, Y Abe, C Shen Nuclear Science and Techniques 29 (12), 172, 2018 | 7 | 2018 |
| Modelling with uncertainties: The role of the fission barrier H Lü, D Boilley EPJ Web of Conferences 62, 03002, 2013 | 7 | 2013 |
| Deep-learning quasi-particle masses from QCD equation of state FP Li, HL Lü, LG Pang, GY Qin Physics Letters B 844, 138088, 2023 | 6 | 2023 |
| Solving Schrodinger equations using a physically constrained neural network KF Pu, HL Li, HL Lü, LG Pang Chinese Physics C 47 (5), 054104, 2023 | 4 | 2023 |
| A machine learning-based approach for failure prediction at cell level based on wafer acceptance test parameters X Chen, Y Zhao, H Lü, X Shao, C Chen, Y Huang 2021 IEEE Microelectronics Design & Test Symposium (MDTS), 1-5, 2021 | 4 | 2021 |
| RCNP-GANIL-Saclay-Huzhou Collaboration on Reaction Theory of Synthesis of SHE Y Abe, D Boilley, CW Shen, BG Giraud, H Lü | | |
