In the context of numerical simulation, a surrogate model approximates the outputs of a solver with a low computational cost. In this article, we present a In Situ visualization prototype, based on Catalyst 2, for monitoring the training of surrogate models based on Deep Neural Networks. In Situ monitoring can help solve a fundamental problem of this kind of training: standard metrics, such as the Mean Squared Error, do not convey enough information on which simulation aspects are harder to learn. Our prototype allows the interactive visualization of the current state of convergence of a physical quantity spatial field, complementing the traditional loss function value curve. We enable the steering of physical parameters during the training process, for interactive exploration. We also allow the user to influence the learning process in real time by changing the learning rate. Results are illustrated in a computational fluids dynamics use case.