This paper presents neural network regression models for predicting the nonlinear static and linearized dynamic reaction forces of spiral grooved gas journal bearings. The partial differential equations (PDEs) are sampled, based on a full factorial and randomly spaced parameter set. Feed-forward neural network (FNN) architectures are developed for modeling the PDEs and therefore replacing the time-consuming discrete and iterative solution procedure used to this date. A significant speed-up factor of >103 in computation time is achieved, compared to solving the PDE numerically. Furthermore, the FNN allows for multi-dimensional interpolation, which makes global system optimization easily possible. This is demonstrated by a real-case rotordynamic system optimization. By using the neural network meta-models, a complete rotordynamic system optimization time reduction of factor 300 is achieved.