The goal of this talk is to give an overview of two new results in the development of hybrid methods for elliptic and hyperbolic partial differential equations (PDEs). A hybrid method combines classical numerical analysis techniques (finite element method (FEM), discontinuous Galerkin (DG), ...) with tools from machine learning (ML). The first part of this talk is dedicated to a broad presentation of such ML tools, including a common framework to represent PDE approximators, be they classical or ML-based. Then, in a second part, we explain how to use a physics-informed prior to lower the error constant of the FEM while keeping the same order of accuracy. Thanks to the FEM framework applied to elliptic PDEs, we rigorously prove that our correction improves the FEM error constant by a factor depending on the prior quality. If time permits, in a third part, we discuss how to enhance the DG basis with physics-informed priors, to increase the resolution of near-equilibrium solutions to hyperbolic systems of balance laws. Once again, we rigorously prove that the error constant is improved. Numerical illustrations will be present throughout the presentation, to validate our results.