The accelerated energy transition has an increasing impact on the need for robust modeling of complex sustainable energy systems such as high-temperature fuel cells. In a number of current research projects, neural network models are used to describe this dynamic system behavior. In this context, the following parameters are considered i.e. gas mass flows, gas inlet temperatures as well as the electric current. These data-driven approaches require further investigations and extensions for a use in a future real-time capable control system besides a classical training of neural network models with point valued out- and input to the layer of the neural network model. To bridge this gap, between only simple trained neural network models of a system and the realisation of a trained robust neural network model of a complex system model, is the target of this work. The assurance of this kind of modeling is demonstrated on the thermal behavior of a high-temperature fuel cell and presented by a Cooperativity Analysis, Stability Analysis and Temporal Variation Rates of Measured and Non-Measured State Variables Analysis of the system model. Finally, a developed correction approach is presented to ensure that a neural network system model of a high temperature fuel cell stack only produces results which are physically meaningful, stable and only with allowable rates of temperature change.