  Physics-informed neural networks (PINNs) is an emerging category of neural
networks which can be trained to solve supervised learning tasks while taking
into consideration given laws of physics described by general nonlinear partial
differential equations. PINNs demonstrate promising characteristics such as
performance and accuracy using minimal amount of data for training, utilized to
accurately represent the physical properties of a system's dynamic environment.
In this work, we employ the emerging paradigm of PINNs to demonstrate their
potential in enhancing the security of intelligent cyberphysical systems. In
particular, we present a proof-of-concept scenario using the use case of water
distribution networks, which involves an attack on a controller in charge of
regulating a liquid pump through liquid flow sensor measurements. PINNs are
used to mitigate the effects of the attack while demonstrating the
applicability and challenges of the approach.
