The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process.Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks.Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models. Neural networks can predict the response of a real physical system to varying input conditions after they have been subjected to an adequate training procedure. Such a network, in combination with physical a priori information embedded in the model structure provides for excellent generalization capability. This takes the form of an appropriate reaction to previously unavailable data. In addition, this enables the model to describe and simulate the behavior of a SI engine under transient and nonlinear conditions.This document describes the approach and its verification of a reference engine under cold start and warm-up conditions.