Artificial neural networks (ANNs) as universal approximators are a frequently applied machine learning instrument. Their popularity is related to their flexibility to be used in various types of ANN architectures . In the field of dynamic system modelling, Maziar Raissi et al. introduced physics informed neural networks (PINN) as a kind of neural networks for solving forward and inverse problems involving nonlinear partial differential equations. They embed the knowledge about physical laws into the loss function and, thus, the trained ANN models satisfy the experimental data as well as the governing equations. Weiqi Ji et al. successfully applied PINN for solving stiff chemical problem .