In addition to the celebrated numerical techniques, such as Finite-Element and Finite-Difference methods, it is also possible to predict the scattering properties of optical components using artificial neural networks. However, these machine-learning models typically suffer from a simplicity versus accuracy trade-off. In our work, we overcome this trade-off. We train several neural networks with an indirect goal. Instead of training the net to predict scattering, we try to train it the laws of Optics on a more fundamental level. In this way, we can increase the predictive power and robustness while maintaining a high degree of transparency in the system.