NeurIPS 2024 ML4CFD竞赛：利用机器学习促进翼型设计中的计算流体动力学
NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
AI总结：
本文介绍NeurIPS 2024 ML4CFD竞赛，依托LIPS框架与AirfRANS数据集，从精度、效率、OOD泛化和物理一致性评估翼型仿真的ML代理模型。
摘要：
将机器学习（ML）技术用于解决复杂物理问题，正日益被视为加速仿真的一条有前景的途径。然而，评估由ML得到的物理模型，对其在工业环境中的采用构成了重大挑战。本竞赛旨在推动用于应对物理挑战的创新ML方法的发展，并利用我们最近推出的统一评估框架Learning Industrial Physical Simulations（LIPS）。在2023年11月至2024年3月举办的初版基础上，本届竞赛聚焦于一项成熟物理应用中的基础任务：翼型设计仿真，并使用我们提出的AirfRANS数据集。竞赛依据多项标准评估参赛方案，包括ML精度、计算效率、分布外（Out-Of-Distribution）性能以及对物理原理的遵循。值得注意的是，本竞赛是探索ML驱动代理方法的开创性努力，旨在优化物理仿真中计算效率与精度之间的权衡。竞赛托管于Codabench平台，为所有参赛方案提供在线训练和评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
