Neural networks as smooth priors for inverse problems for PDEs

In this paper we discuss the potential of using artificial neural networks as smooth priors in classical methods for inverse problems PDEs. Exploring that are global and function approximators, idea is could act attractive coefficients to be estimated from noisy data. We illustrate capabilities context Poisson equation show network approach robustness with respect noisy, incomplete data mesh geometry.