Abstract: The talk will present two engineering applications in which non-intrusive implementations of ROM techniques in commercial codes have been performed. The first application deals with the prediction of remaining useful life (RUL) of IGBT power electronic modules, which are essential components to numerous electrical systems. During their operation, losses generate heat within the module, leading to thermal stress, and eventually resulting in component failure. In order to obtain a physics-based computational model which is compatible with real-time RUL prediction, and which accounts for the numerous uncertainty sources, a parametrized multi-physics (electro-thermo-mechanical) reduced model is developed for IGBT power modules. It is based on the Proper Generalized Decomposition (PGD) method, and it is implemented in a non-intrusive version in Ansys. RUL estimation and uncertainty quantification are then performed from the assimilation of experimental measures, by means of Bayesian inference and transport maps sampling. The second application tackles the challenge of effective modeling and simulation for large mechanical structures exhibiting numerous local complex behaviors, here spot welds in automative crash numerical analysis. We propose a non-intrusive local/global model coupling strategy, in which the local model is a neural network-based reduced model, specifically a Physics-Guided Neural Network (PGANN). The proposed strategy, implemented in OpenRadioss, does not modify the global solver. It enables accurate simulations on complex 3D industrial structures with multiple spot welds, while maintaining computational efficiency. During the talk, methodological and technical aspects of these two non-intrusive ROM applications will be discussed.