Physically Interpretable Machine Learning for nuclear masses

M. R. Mumpower, T. M. Sprouse, A. E. Lovell +1

We present a novel approach to modeling the ground state mass of atomic nuclei based directly on a probabilistic neural network constrained by relevant physics. Our Physically Inte…