  The use of machine learning (ML) techniques to solve complex physical
problems has been considered recently as a promising approach. However, the
evaluation of such learned physical models remains an important issue for
industrial use. The aim of this competition is to encourage the development of
new ML techniques to solve physical problems using a unified evaluation
framework proposed recently, called Learning Industrial Physical Simulations
(LIPS). We propose learning a task representing a well-known physical use case:
the airfoil design simulation, using a dataset called AirfRANS. The global
score calculated for each submitted solution is based on three main categories
of criteria covering different aspects, namely: ML-related,
Out-Of-Distribution, and physical compliance criteria. To the best of our
knowledge, this is the first competition addressing the use of ML-based
surrogate approaches to improve the trade-off computational cost/accuracy of
physical simulation.The competition is hosted by the Codabench platform with
online training and evaluation of all submitted solutions.
