  This paper presents a methodology to learn surrogate models of steady state
fluid dynamics simulations on meshed domains, based on Implicit Neural
Representations (INRs). The proposed models can be applied directly to
unstructured domains for different flow conditions, handle non-parametric 3D
geometric variations, and generalize to unseen shapes at test time. The
coordinate-based formulation naturally leads to robustness with respect to
discretization, allowing an excellent trade-off between computational cost
(memory footprint and training time) and accuracy. The method is demonstrated
on two industrially relevant applications: a RANS dataset of the
two-dimensional compressible flow over a transonic airfoil and a dataset of the
surface pressure distribution over 3D wings, including shape, inflow condition,
and control surface deflection variations. On the considered test cases, our
approach achieves a more than three times lower test error and significantly
improves generalization error on unseen geometries compared to state-of-the-art
Graph Neural Network architectures. Remarkably, the method can perform
inference five order of magnitude faster than the high fidelity solver on the
RANS transonic airfoil dataset. Code is available at
https://gitlab.isae-supaero.fr/gi.catalani/aero-nepf
