报告题目：CFDONEval : A comprehensive evaluation of operator-learning neural network models for computational fluid dynamics

报告人： 邹青松 教授（中山大学）

报告时间：2025年5月23日14:30-15:30

报告地点：格物楼528

报告摘要：

    In this talk, we introduce CFDONEval, a comprehensive evaluation of 12 operator-learning-based neural network (ON) models to simulate 7 benchmark fluid dynamics problems. These problems cover a range of 2D scenarios, including Darcy flow, two-phase flow, Taylor-Green vortex, lid-driven cavity flow, tube flow, circular cylinder flow, and 3D periodic hill flow. For a rigorous evaluation, we establish 22 fluid dynamics datasets for these benchmark problems, 18 of which are newly generated using traditional numerical methods, such as the finite element method. Our evaluation tackles 5 key challenges: multiscale phenomena, convection dominance, long-term predictions, multiphase flows, and unstructured meshes over complex geometries. We assess computational accuracy, efficiency, and flow field visualization, offering valuable insights into the application of ON models in fluid dynamics research. Our findings show that attention-based models perform well in handling almost all challenges; models with a U-shaped structure excel in handling multiscale problems; and the NU-FNO model demonstrates the smallest relative error in $L_2$ norm when processing nonuniform grid data. The associated code and datasets will be released publicly.

报告人简介：

    邹青松，教授、中山大学计算机学院科学计算系主任、广东省计算数学学会理事长。研究领域包括偏微分方程的传统计算方法和人工智能方法。在传统计算方法领域，对于高阶高精度有限体积法有较深入系统研究。在人工智能计算领域，曾构造自适应深度神经网络方法、正反向随机时序差分算法，深度有限体积法等在SlAMJNumer Anal,Math Comp, Numer Math, JComp Phys等期刊发表论文70多篇。主持国家自然科学基金面上项目、国家科技部科技创新重大项目课题、广东省自然科学基金重点和面上项目等。获评2020年获广东省自然科学二等奖(排名第1)。