Citing Articles For: Physically interpretable machine learning for nuclear masses

M. R. Mumpower, T. M. Sprouse, T. Kawano, M. W. Herman, A. E. Lovell, G. W. Misch, D. Neudecker, H. Sasaki, I. Stetcu, P. Talou, C.M. Mattoon, R. Vogt, J. Escher, and I. Thompson

EPJ Web of Conf. 284 12001 (2023)

Examination of machine learning for assessing physical effects: Learning the relativistic continuum mass table with kernel ridge regression*

Xiao-Kai Du, Peng Guo, Xin-Hui Wu, and Shuang-Quan Zhang

Chinese Phys. C 47 074108 (2023)

Evaporation residue cross sections of superheavy nuclei based on optimized nuclear data*

Jia-Xing 佳星 Li 李 and Hong-Fei 鸿飞 Zhang 张

Chinese Phys. C 47 124105 (2023)

Estimations for the Production Cross Sections of Medical 61, 64, 67Cu Radioisotopes by Using Bayesian Regularized Artificial Neural Networks in (p, α) Reactions

Yiğit Ali Üncü and Hasan Özdoğan

Arab J Sci Eng 48 8173 (2023)

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

Marc Verriere, Nicolas Schunck, Irene Kim, Petar Marević, Kevin Quinlan, Michelle N. Ngo, David Regnier, and Raphael David Lasseri

Front. Phys. 10 1028370 (2022)

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Physically interpretable machine learning for nuclear masses

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Improved nuclear mass formula with an additional term from the Fermi gas model

Revisiting the island of hexadecapole-deformation nuclei in the A ≈ 150 mass region: focusing on the model application to nuclear shapes and masses

Prediction of binding energy using machine learning approach

Machine learning in nuclear physics at low and intermediate energies

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Analysis of a Skyrme energy density functional with deep learning

Multiple constraints on nuclear mass formulas for reliable extrapolations

Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

Precise mass measurements of radioactive nuclides for astrophysics

Nuclear data activities for medium mass and heavy nuclei at Los Alamos

Examination of machine learning for assessing physical effects: Learning the relativistic continuum mass table with kernel ridge regression*

Evaporation residue cross sections of superheavy nuclei based on optimized nuclear data*

Estimations for the Production Cross Sections of Medical 61, 64, 67Cu Radioisotopes by Using Bayesian Regularized Artificial Neural Networks in (p, α) Reactions

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

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