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.

翻译：近年来，利用机器学习技术解决复杂物理问题被认为是一种有前景的方法。然而，此类学习型物理模型在工业应用中的评估仍是一个重要问题。本竞赛旨在鼓励开发新的机器学习技术，通过近期提出的统一评估框架——学习型工业物理仿真（LIPS）来解决物理问题。我们提出利用名为AirfRANS的数据集，学习一个代表著名物理应用场景的任务：翼型设计仿真。每个提交解决方案的全局得分基于三大类覆盖不同方面的标准，即：机器学习相关标准、分布外标准及物理一致性标准。据我们所知，这是首个探讨利用基于机器学习的替代方法来改善物理仿真计算成本与精度权衡的竞赛。该竞赛由Codabench平台托管，对所有提交方案进行在线训练与评估。