  This paper introduces a physics-informed machine learning approach for
pathloss prediction. This is achieved by including in the training phase
simultaneously (i) physical dependencies between spatial loss field and (ii)
measured pathloss values in the field. It is shown that the solution to a
proposed learning problem improves generalization and prediction quality with a
small number of neural network layers and parameters. The latter leads to fast
inference times which are favorable for downstream tasks such as localization.
Moreover, the physics-informed formulation allows training and prediction with
a small amount of training data which makes it appealing for a wide range of
practical pathloss prediction scenarios.
