相关论文: The East Lansing Model: a Bayesian uncertainty qua…

We derive local microscopic optical potentials $U$ systematically for polarized proton scattering at 65~MeV using the local-potential version of the Melbourne $g$-matrix folding model. As target nuclei, we take $^{6}$He and neutron-rich Ne…

We study elastic proton-nucleus scattering at intermediate energies. The nucleon-nucleus optical potential is derived from the Bonn nucleon-nucleon potential and the Dirac-Brueckner approach for nuclear matter. Our calculations, which do…

The boundary element method (BEM) enables solving three-dimensional electromagnetic problems using a two-dimensional surface mesh, making it appealing for applications ranging from electrical interconnect analysis to the design of…

The neutron-proton effective $k$-mass splitting in asymmetric nucleonic matter of isospin asymmetry $\delta$ and normal density is found to be $m^{*}_{n-p}\equiv(m^{*}_{n}-m^{*}_{p})/m=(0.41 \pm0.15)\delta$ from analyzing globally 1088 sets…

In the study of $\alpha$ decay within the superheavy nuclear region ($Z \geq 90$ and $N \geq 140$), the $\alpha$-particle preformation probability $P_{\alpha}$ serves as a crucial physical quantity linking nuclear structure to decay…

We evaluate the uncertainty quality in neural networks using anomaly detection. We extract uncertainty measures (e.g. entropy) from the predictions of candidate models, use those measures as features for an anomaly detector, and gauge how…

Microlensing of extragalactic sources, in particular the probability of significant amplifications, is a potentially powerful probe of the abundance of compact objects outside the halo of the Milky Way. Accurate experimental constraints…

In this proceeding, we have presented some highlight results on the constraints of the nuclear matter equation of state (EOS) from the data of nucleus resonance and neutron-skin thickness using the Bayesian approach based on the…

Parity-violating polarized electron scattering from nucleons and nuclei provides an excellent tool to extract valuable information on nuclear and nucleon structure, as well as to determine Standard Model couplings and higher-order radiative…

Predictions of nuclear properties far from measured data are inherently imprecise because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model…

A study of the interaction of loosely bound nuclei 6,7Li at 9 and 19 AMeV with light targets has been undertaken. With the determination of unambiguous optical potentials in mind, elastic data for four projectile-target combinations and one…

Using various relativistic mean-field models, including the nonlinear ones with meson field self-interactions, those with density-dependent meson-nucleon couplings, and the point-coupling models without meson fields, we have studied the…

From a systems biology perspective the majority of cancer models, although interesting and providing a qualitative explanation of some problems, have a major disadvantage in that they usually miss a genuine connection with experimental…

Elastic scattering angular distributions for systems with reduced mass between 3 and 34 and energies varying between 25 and 120 MeV/nucleon were analyzed. The stable $^4$He, its exotic partner $^6$He, and the weakly bound $^{6,7}$Li nuclei…

Being able to rigorously quantify the uncertainties in reaction models is crucial to moving this field forward. Even though Bayesian methods are becoming increasingly popular in nuclear theory, they are yet to be implemented and applied in…

Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns…

Two existing global medium-energy nucleon-nucleus phenomenological optical model potentials are described and compared with experiment and with each other. The first of these employs a Dirac approach (second-order reduction) that is global…

Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using…

Scientific machine learning increasingly uses spectral methods to understand physical systems. Current spectral learning approaches provide only point estimates without uncertainty quantification, limiting their use in safety-critical…

The recent and older level shifts and widths in pbar atoms are analyzed. The results are fitted by an antiproton-nucleus optical potential with two basic complex strength parameters. These parameters are related to average S and P wave…