空气动力学
计算流体力学
计算机科学
大涡模拟
杠杆(统计)
湍流
雷诺平均Navier-Stokes方程
雷诺数
数值天气预报
计算科学
强迫(数学)
流体力学
维数(图论)
流体力学
算法
人工智能
数学
物理
机械
气象学
数学分析
纯数学
作者
Dmitrii Kochkov,Jamie Smith,Ayya Alieva,Qing Wang,Michael P. Brenner,Stephan Hoyer
标识
DOI:10.1073/pnas.2101784118
摘要
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large eddy simulation, our results are as accurate as baseline solvers with 8-10x finer resolution in each spatial dimension, resulting in 40-80x fold computational speedups. Our method remains stable during long simulations, and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black box machine learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.
科研通智能强力驱动
Strongly Powered by AbleSci AI