  Estimating fluid dynamics is classically done through the simulation and
integration of numerical models solving the Navier-Stokes equations, which is
computationally complex and time-consuming even on high-end hardware. This is a
notoriously hard problem to solve, which has recently been addressed with
machine learning, in particular graph neural networks (GNN) and variants
trained and evaluated on datasets of static objects in static scenes with fixed
geometry. We attempt to go beyond existing work in complexity and introduce a
new model, method and benchmark. We propose EAGLE, a large-scale dataset of 1.1
million 2D meshes resulting from simulations of unsteady fluid dynamics caused
by a moving flow source interacting with nonlinear scene structure, comprised
of 600 different scenes of three different types. To perform future forecasting
of pressure and velocity on the challenging EAGLE dataset, we introduce a new
mesh transformer. It leverages node clustering, graph pooling and global
attention to learn long-range dependencies between spatially distant data
points without needing a large number of iterations, as existing GNN methods
do. We show that our transformer outperforms state-of-the-art performance on,
both, existing synthetic and real datasets and on EAGLE. Finally, we highlight
that our approach learns to attend to airflow, integrating complex information
in a single iteration.
