  A microscopic description of the interaction of atomic nuclei with external
electroweak probes is required for elucidating aspects of short-range nuclear
dynamics and for the correct interpretation of neutrino oscillation
experiments. Nuclear quantum Monte Carlo methods infer the nuclear electroweak
response functions from their Laplace transforms. Inverting the Laplace
transform is a notoriously ill-posed problem; and Bayesian techniques, such as
maximum entropy, are typically used to reconstruct the original response
functions in the quasielastic region. In this work, we present a
physics-informed artificial neural network architecture suitable for
approximating the inverse of the Laplace transform. Utilizing simulated, albeit
realistic, electromagnetic response functions, we show that this
physics-informed artificial neural network outperforms maximum entropy in both
the low-energy transfer and the quasielastic regions, thereby allowing for
robust calculations of electron scattering and neutrino scattering on nuclei
and inclusive muon capture rates.
