Examples of Machine Learning methods and applications in nuclear physics, continues

  • Low-energy nuclear reactions UQ: Bayesian optimization studies of the nucleon-nucleus optical potential, R-matrix analyses, and statistical spatial networks to study patterns in nuclear reaction networks.
  • Neutron star properties and nuclear matter equation of state: constraining the equation of state by properties on neutron stars and selected properties of finite nuclei
  • Experimental design: Bayesian ML provides a framework to maximize the success of on experiment based on the best information available on existing data, experimental conditions, and theoretical models.