  Model Order Reduction (MOR) methods enable the generation of
real-time-capable digital twins, which can enable various novel value streams
in industry. While traditional projection-based methods are robust and accurate
for linear problems, incorporating Machine Learning to deal with nonlinearity
becomes a new choice for reducing complex problems. Such methods usually
consist of two steps. The first step is dimension reduction by projection-based
method, and the second is the model reconstruction by Neural Network. In this
work, we apply some modifications for both steps respectively and investigate
how they are impacted by testing with three simulation models. In all cases
Proper Orthogonal Decomposition (POD) is used for dimension reduction. For this
step, the effects of generating the input snapshot database with constant input
parameters is compared with time-dependent input parameters. For the model
reconstruction step, two types of neural network architectures are compared:
Multilayer Perceptron (MLP) and Runge-Kutta Neural Network (RKNN). The MLP
learns the system state directly while RKNN learns the derivative of system
state and predicts the new state as a Runge-Kutta integrator.
