Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …

The theory of the nuclear energy-density functional is used to provide a unified and
thermodynamically consistent treatment of all regions of cold non-accreting neutron stars. In …

Bayesian neural network (BNN) approach is employed to improve the nuclear mass
predictions of various models. It is found that the noise error in the likelihood function plays …

Background: The mass, or binding energy, is the basis property of the atomic nucleus. It
determines its stability and reaction and decay rates. Quantifying the nuclear binding is …

Background: The masses of ∼∼ 2500 nuclei have been measured experimentally;
however,> 7000 isotopes are predicted to exist in the nuclear landscape from H (Z= 1 Z= 1) …

Nuclear masses are predicted with the Bayesian neural networks by learning the mass
surface of even-even nuclei and the correlation energies to their neighboring nuclei. By …

The region of heavy calcium isotopes forms the frontier of experimental and theoretical
nuclear structure research where the basic concepts of nuclear physics are put to stringent …

We describe the Bayesian analysis of nuclear dynamics (BAND) framework, a
cyberinfrastructure that we are developing which will unify the treatment of nuclear models …

This is an exciting time for the study of r-process nucleosynthesis. Recently, a neutron star
merger GW170817 was observed in extraordinary detail with gravitational waves and …