In this talk, we will present an overview of recent works aiming at solving inverse problems (state and parameter estimation) by combining optimally measurement observations and parametrized PDE models. After defining a notion of optimal performance in terms of the smallest possible reconstruction error that any reconstruction algorithm can achieve, we will present practical numerical algorithms based on nonlinear reduced models for which we can prove that they can deliver a performance close to optimal.