[1] Carleo G, Cirac I, Cranmer K et al. Machine learning and the physical sciences[J]. Reviews of Modern Physics, 91, 045002(2019).

[2] Boehnlein A, Diefenthaler M, Sato N et al. Machine learning in nuclear physics[J]. Reviews of Modern Physics, 94, 031003(2022).

[3] Niu Z M, Liang H Z. Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects[J]. Physics Letters B, 778, 48-53(2018).

[4] Niu Z M, Liang H Z. Nuclear mass predictions with machine learning reaching the accuracy required by r-process studies[J]. Physical Review C, 106, L021303(2022).

[5] Wu X H, Lu Y Y, Zhao P W. Multi-task learning on nuclear masses and separation energies with the kernel ridge regression[J]. Physics Letters B, 834, 137394(2022).

[6] Wei H L, Zhu X, Yuan C. Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei[J]. Nuclear Science and Techniques, 33, 111(2022).

[7] Ma Y F, Su C, Liu J et al. Predictions of nuclear charge radii and physical interpretations based on the naive Bayesian probability classifier[J]. Physical Review C, 101, 014304(2020).

[8] Wu D, Bai C L, Sagawa H et al. Calculation of nuclear charge radii with a trained feed-forward neural network[J]. Physical Review C, 102, 054323(2020).

[9] Niu Z M, Liang H Z, Sun B H et al. Predictions of nuclear β-decay half-lives with machine learning and their impact on r-process nucleosynthesis[J]. Physical Review C, 99, 064307(2019).

[10] Li C Q, Tong C N, Du H J et al. Deep learning approach to nuclear masses and α-decay half-lives[J]. Physical Review C, 105, 064306(2022).

[11] Zhao T L, Zhang H F. Unified description of α decay and cluster radioactivity using the neural network approach and universal decay law[J]. Journal of Physics G: Nuclear and Particle Physics, 49, 105104(2022).

[12] Qiao C Y, Pei J C, Wang Z A et al. Bayesian evaluation of charge yields of fission fragments of 239U[J]. Physical Review C, 103, 034621(2021).

[13] Wang Z A, Pei J C, Liu Y et al. Bayesian evaluation of incomplete fission yields[J]. Physical Review Letters, 123, 122501(2019).

[14] Ma C W, Wei X B, Chen X X et al. Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks[J]. Chinese Physics C, 46, 074104(2022).

[15] Ma C W, Peng D, Wei H L et al. Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method[J]. Chinese Physics C, 44, 014104(2020).

[16] Alhassan E, Rochman D, Vasiliev A et al. Iterative Bayesian Monte Carlo for nuclear data evaluation[J]. Nuclear Science and Techniques, 33, 50(2022).

[17] Wang X H, Zhu L, Su J. Providing physics guidance in Bayesian neural networks from the input layer: the case of giant dipole resonance predictions[J]. Physical Review C, 104, 034317(2021).

[18] He J J, He W B, Ma Y G et al. Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions[J]. Physical Review C, 104, 044902(2021).

[19] Xu J, Xie W J, Li B A. Bayesian inference of nuclear symmetry energy from measured and imagined neutron skin thickness in 116, 118, 120, 122, 124, 130, 132Sn, 208Pb, and 48Ca[J]. Physical Review C, 102, 044316(2020).

[20] Zhang Z, Feng X B, Chen L W. Bayesian inference on isospin splitting of nucleon effective mass from giant resonances in 208Pb[J]. Chinese Physics C, 45, 064104(2021).

[21] Li X Z, Zhang Q X, Tan H Y et al. Fast nuclide identification based on a sequential Bayesian method[J]. Nuclear Science and Techniques, 32, 143(2021).

[22] Pang L G, Zhou K, Su N et al. An equation-of-state-meter of quantum chromodynamics transition from deep learning[J]. Nature Communications, 9, 210(2018).

[23] Li Y Y, Zhang F, Su J. Improvement of the Bayesian neural network to study the photoneutron yield cross sections[J]. Nuclear Science and Techniques, 33, 135(2022).

[24] Song Y D, Wang R, Ma Y G et al. Determining the temperature in heavy-ion collisions with multiplicity distribution[J]. Physics Letters B, 814, 136084(2021).

[25] Gao Z P, Wang Y J, Lü H L et al. Machine learning the nuclear mass[J]. Nuclear Science and Techniques, 32, 109(2021).

[26] Li F P, Wang Y J, Gao Z P et al. Application of machine learning in the determination of impact parameter in the 132Sn+124Sn system[J]. Physical Review C, 104, 034608(2021).

[27] Li F P, Wang Y J, Lü H L et al. Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies[J]. Journal of Physics G: Nuclear and Particle Physics, 47, 115104(2020).

[28] Wang Y J, Gao Z P, Lü H L et al. Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning[J]. Physics Letters B, 835, 137508(2022).

[29] Wang Y J, Li F P, Li Q F et al. Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning[J]. Physics Letters B, 822, 136669(2021).

[30] Bass S. Microscopic models for ultrarelativistic heavy ion collisions[J]. Progress in Particle and Nuclear Physics, 41, 255-369(1998).

[31] Wang Y J, Li Q F. Application of microscopic transport model in the study of nuclear equation of state from heavy ion collisions at intermediate energies[J]. Frontiers of Physics, 15, 44302(2020).

[32] Zhang Y X, Wang N, Li Q F et al. Progress of quantum molecular dynamics model and its applications in heavy ion collisions[J]. Frontiers of Physics, 15, 54301(2020).

[33] GAO Zepeng, WANG Yongjia, LI Qingfeng et al. Application of machine learning to study the effects of quadrupole deformation on the nucleus in heavy-ion collisions at intermediate energies[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 52, 96-106(2022).

[34] LI Fupeng, WANG Yongjia, LI Qingfeng. Using deep learning to study the equation of state of nuclear matter[J]. Nuclear Physics Review, 37, 825-832(2020).

[35] Wang M, Audi G, Kondev F G et al. The AME2016 atomic mass evaluation (II). tables, graphs and references[J]. Chinese Physics C, 41, 030003(2017).

[36] Wang M, Huang W J, Kondev F G et al. The AME 2020 atomic mass evaluation (II). tables, graphs and references[J]. Chinese Physics C, 45, 030003(2021).

[37] Bass S A, Bischoff A, Maruhn J A et al. Neural networks for impact parameter determination[J]. Physical Review C, 53, 2358-2363(1996).

[38] Ma Y G. New ttype of double‑slitt intterference experimentt at Fermi scale[J]. Nuclear Science and Techniques, 34, 16(2023).

[39] Wang X N. Vector meson spin alignment by the strong force feld[J]. Nuclear Science and Techniques, 34, 15(2023).