  Reduced-order models that accurately abstract high fidelity models and enable
faster simulation is vital for real-time, model-based diagnosis applications.
In this paper, we outline a novel hybrid modeling approach that combines
machine learning inspired models and physics-based models to generate
reduced-order models from high fidelity models. We are using such models for
real-time diagnosis applications. Specifically, we have developed machine
learning inspired representations to generate reduced order component models
that preserve, in part, the physical interpretation of the original high
fidelity component models. To ensure the accuracy, scalability and numerical
stability of the learning algorithms when training the reduced-order models we
use optimization platforms featuring automatic differentiation. Training data
is generated by simulating the high-fidelity model. We showcase our approach in
the context of fault diagnosis of a rail switch system. Three new model
abstractions whose complexities are two orders of magnitude smaller than the
complexity of the high fidelity model, both in the number of equations and
simulation time are shown. The numerical experiments and results demonstrate
the efficacy of the proposed hybrid modeling approach.
