This project aims to develop methods and toolchains for computationally efficient and precise modeling of large-scale systems. In the first year of the project, a spatially discretized heat exchanger system model with phase change was used as a testbed for exploring novel model reduction methods and constrained machine learning (ML) algorithms. These two approaches were used for the synthesis of reduced models of a heat exchanger, which were tested at nominal and T-optimal conditions (the latter being conditions within the system allowable state space that maximize the error of the reduced, machine learning or hybrid models, as compared to the original model). The project is performed in collaboration with Pratt & Whitney and UTAS, with the former leading the effort. P&W has identified the application of interest for the developed method in aerospace engine systems. There is an identified need for simplified, accurate system models for real-time applications. Therefore, the technology, methods and tools developed in the first year of the project are focused on reducing model complexity and improving model accuracy. The ongoing effort seeks to combine machine learning and model reduction techniques in order to create useful hybrid models, and to further develop the individual tools and techniques applied.