Copertina

Robust extrapolation using physics-related activation functions in neural networks for nuclear masses

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kim, C. H., Chae, K. Y., Smith, M. S.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Nuclear Theory
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Validation and extrapolation of atomic mass with physics-informed fully connected neural network
di: Huang, Yiming, et al.
Pubblicazione: (2025)

Architecture as physical prior: cooperative neural network for nuclear masses
di: Zai, Peiwen, et al.
Pubblicazione: (2026)

Probabilistic neural networks for improved analyses with phenomenological models
di: Kim, C. H., et al.
Pubblicazione: (2023)

The mass of $^{101}$Sn and Bayesian extrapolations to the proton drip line
di: Ireland, Christian M., et al.
Pubblicazione: (2025)

Highly efficient nuclear population transfer through physics-informed neural networks
di: Liu, Jing, et al.
Pubblicazione: (2025)

Global prediction of nuclear charge density distributions using deep neural network
di: Shang, Tian Shuai, et al.
Pubblicazione: (2024)

Principal components of nuclear mass models
di: Wu, X. H., et al.
Pubblicazione: (2024)

Systematic Bayesian Evaluation of Resonance Parameters in 19Ne for the 15O(alpha,gamma)19Ne and 18F(p,alpha)15O Reactions
di: Kim, S. H., et al.
Pubblicazione: (2025)

Matter radii from interaction cross sections using microscopic nuclear densities
di: Smith, A. J., et al.
Pubblicazione: (2026)

Shell quenching in nuclear charge radii based on Monte Carlo dropout Bayesian neural network
di: Xian, Zhen-Yan, et al.
Pubblicazione: (2024)

Glauber-theory calculations of high-energy nuclear scattering observables using variational Monte Carlo wave functions
di: Horiuchi, W., et al.
Pubblicazione: (2025)

Learning nuclear cross sections across the chart of nuclides with graph neural networks
di: Choi, Hongjun, et al.
Pubblicazione: (2024)

Nuclear mass predictions based on convolutional neural network
di: Lu, Yanhua, et al.
Pubblicazione: (2024)

Benchmarking nuclear energy density functionals with new mass data
di: Qu, Xiaoying, et al.
Pubblicazione: (2025)

Data-driven analysis of dipole strength functions using artificial neural networks
di: Jiang, Weiguang, et al.
Pubblicazione: (2024)

Microscopic determination of the interacting boson-fermion model Hamiltonian from the nuclear energy density functional
di: Homma, M., et al.
Pubblicazione: (2025)

Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)
di: Yiu, To Chung, et al.
Pubblicazione: (2023)

Towards accurate nuclear mass tables in covariant density functional theory
di: Taninah, A., et al.
Pubblicazione: (2024)

Configuration-interaction time-dependent density functional theory for nuclear dynamics
di: Wang, Y. P., et al.
Pubblicazione: (2026)

Folding potential with modern nuclear density functionals and application to 16O+208Pb reaction
di: Heo, Kyoungsu, et al.
Pubblicazione: (2024)

Relativistic configuration-interaction density functional theory: Nonaxial effects on nuclear $ββ$ decay
di: Wang, Y. K., et al.
Pubblicazione: (2023)

Direct local parametrization of nuclear state densities using the back-shifted Bethe formula
di: Özen, C., et al.
Pubblicazione: (2024)

First observation of multi-phonon $γ$-vibrations in an odd-odd nuclear system
di: Wang, E. H., et al.
Pubblicazione: (2026)

Deep learning for nuclear masses in deformed relativistic Hartree-Bogoliubov theory in continuum
di: Choi, Soonchul, et al.
Pubblicazione: (2024)

A nuclear mass model rooted in chiral effective field theory
di: Mishra, C., et al.
Pubblicazione: (2025)

Robust linear correlations related to neutron skin thickness
di: Lei, Y., et al.
Pubblicazione: (2025)

The 25th anniversary for nuclear chirality
di: Meng, J., et al.
Pubblicazione: (2024)

The study of $0νββ$ decay of $^{136}$Xe using nonclosure approach in nuclear shell model
di: Sarkar, S., et al.
Pubblicazione: (2024)

Optimized nuclear energy density functionals including long-range pion contributions
di: Zurek, L., et al.
Pubblicazione: (2023)

Multi-task deep neural network for predicting both nuclear fission yields and their experimental errors in peak-shaped data
di: Ueno, Maomi, et al.
Pubblicazione: (2026)

Criticality analysis of nuclear binding energy neural networks
di: Sundberg, S. A., et al.
Pubblicazione: (2025)

Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables
di: Tarasov, O. B.
Pubblicazione: (2026)

Robust correlation between binding energies and charge radii of mirror nuclei
di: Lei, Y., et al.
Pubblicazione: (2024)

The DNA of nuclear models: How AI predicts nuclear masses
di: Richardson, Kate A., et al.
Pubblicazione: (2025)

Interrogating the composition and distribution of nuclear magnetization via the hyperfine anomaly: experiment meets nuclear and atomic theory for short-lived $^{47}$K
di: Bissell, M. L., et al.
Pubblicazione: (2026)

Computing nuclear response functions with time-dependent coupled-cluster theory
di: Bonaiti, Francesca, et al.
Pubblicazione: (2025)

An implementation of nuclear many-body wave functions by the superposition of localized Gaussians
di: Kimura, Masaaki, et al.
Pubblicazione: (2024)

Dual-polarization structure and nuclear structure effect on $Λ$ polarization
di: Deng, X. G., et al.
Pubblicazione: (2025)

Role of nuclear and electromagnetic fragmentation in the charge-changing reactions of 18O on carbon and lead targets at around 370 MeV/nucleon
di: Liu, J. R., et al.
Pubblicazione: (2025)

Glauber-theory analysis of nuclear reactions on 12C target with variational Monte Carlo wave functions
di: Horiuchi, W., et al.
Pubblicazione: (2025)