Speaker
Description
Gaussian process (GP) regression is a powerful nonparametric Bayesian method that provides both predictions and principled uncertainty estimates in closed form. While most applications rely on generic priors, the flexibility of the GP framework allows physics knowledge to be systematically encoded into the mean function and the kernel. We exploit this capability in the context of nuclear physics, applying physics-informed GP models to three problems: nuclear binding energies, the finite-temperature equation of state of dense matter, and nucleon-nucleon scattering phase shifts. In each case, we demonstrate that encoding known theoretical structures yields substantial and systematic improvements in interpolation accuracy, uncertainty calibration, and extrapolation reliability over agnostic baselines. Our results highlight that the design of the mean function prior can be as consequential as the choice of kernel, offering a general strategy for physics-informed machine learning of complex quantum systems.