相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Machine learning methods for turbulence modeling in subsonic flows around airfoils
Linyang Zhu et al.
PHYSICS OF FLUIDS (2019)
State-of-the-art in aerodynamic shape optimisation methods
S. N. Skinner et al.
APPLIED SOFT COMPUTING (2018)
Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder
Xiaowei Jin et al.
PHYSICS OF FLUIDS (2018)
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Jin-Long Wu et al.
PHYSICAL REVIEW FLUIDS (2018)
Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation
Zhuo Wang et al.
PHYSICS OF FLUIDS (2018)
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
R. Maulik et al.
PHYSICS OF FLUIDS (2018)
Using field inversion to quantify functional errors in turbulence closures
Anand Pratap Singh et al.
PHYSICS OF FLUIDS (2016)
Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system
Ming Ma et al.
PHYSICS OF FLUIDS (2015)
Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
J. Ling et al.
PHYSICS OF FLUIDS (2015)
分享论文
