  Physics-Informed Neural Networks (PINNs) are Neural Network architectures
trained to emulate solutions of differential equations without the necessity of
solution data. They are currently ubiquitous in the scientific literature due
to their flexible and promising settings. However, very little of the available
research provides practical studies that aim for a better quantitative
understanding of such architecture and its functioning. In this paper, we
perform an empirical analysis of the behavior of PINN predictions outside their
training domain. The primary goal is to investigate the scenarios in which a
PINN can provide consistent predictions outside the training area.
Thereinafter, we assess whether the algorithmic setup of PINNs can influence
their potential for generalization and showcase the respective effect on the
prediction. The results obtained in this study returns insightful and at times
counterintuitive perspectives which can be highly relevant for architectures
which combines PINNs with domain decomposition and/or adaptive training
strategies.
