  Physics-Informed Neural Networks (PINNs) have recently emerged as a novel
approach to simulate complex physical systems on the basis of both data
observations and physical models. In this work, we investigate the use of PINNs
for various applications in aerodynamics and we explain how to leverage their
specific formulation to perform some tasks effectively. In particular, we
demonstrate the ability of PINNs to construct parametric surrogate models, to
achieve multiphysic couplings and to infer turbulence characteristics via data
assimilation. The robustness and accuracy of the PINNs approach are analysed,
then current issues and challenges are discussed.
