The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However, assessing ML-derived physical models poses a significant challenge for their adoption within industrial contexts. This competition is designed to promote the development of innovative ML approaches for tackling physical challenges, leveraging our recently introduced unified evaluation framework known as Learning Industrial Physical Simulations (LIPS). Building upon the preliminary edition held from November 2023 to March 2024, this iteration centers on a task fundamental to a well-established physical application: airfoil design simulation, utilizing our proposed AirfRANS dataset. The competition evaluates solutions based on various criteria encompassing ML accuracy, computational efficiency, Out-Of-Distribution performance, and adherence to physical principles. Notably, this competition represents a pioneering effort in exploring ML-driven surrogate methods aimed at optimizing the trade-off between computational efficiency and accuracy in physical simulations. Hosted on the Codabench platform, the competition offers online training and evaluation for all participating solutions.

翻译：将机器学习技术用于解决复杂物理问题，正日益被视为加速仿真的一个前景广阔的途径。然而，评估机器学习衍生的物理模型，对其在工业环境中的应用构成了重大挑战。本次竞赛旨在推动开发创新的机器学习方法以应对物理挑战，并利用我们最近引入的统一评估框架——工业物理仿真学习。在2023年11月至2024年3月举办的初步版本基础上，本次迭代聚焦于一个成熟物理应用中的基础任务：翼型设计仿真，并采用我们提出的AirfRANS数据集。竞赛根据多项标准评估解决方案，涵盖机器学习准确性、计算效率、分布外性能以及对物理原理的遵循。值得注意的是，本次竞赛是探索机器学习驱动的代理方法以优化物理仿真中计算效率与准确性之间权衡的开创性尝试。竞赛在Codabench平台上举办，为所有参赛解决方案提供在线训练和评估。