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。Experimental velocity data estimation for imperfect particle images using machine learning
Masaki Morimoto et al.
PHYSICS OF FLUIDS (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Takaaki Murata et al.
JOURNAL OF FLUID MECHANICS (2020)
Deep learning methods for super-resolution reconstruction of turbulent flows
Bo Liu et al.
PHYSICS OF FLUIDS (2020)
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Maziar Raissi et al.
SCIENCE (2020)
Assessment of supervised machine learning methods for fluid flows
Kai Fukami et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
Kazuto Hasegawa et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Reconstructing the time evolution of wall-bounded turbulent flows from non-time-resolved PIV measurements
C. Vamsi Krishna et al.
PHYSICAL REVIEW FLUIDS (2020)
Special issue on machine learning and data-driven methods in fluid dynamics
Steven L. Brunton et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2020)
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
Kai Fukami et al.
PHYSICS OF FLUIDS (2020)
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
Romit Maulik et al.
PHYSICAL REVIEW FLUIDS (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Super-resolution reconstruction of turbulent flows with machine learning
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2019)
Predictions of turbulent shear flows using deep neural networks
P. A. Srinivasan et al.
PHYSICAL REVIEW FLUIDS (2019)
Synthetic turbulent inflow generator using machine learning
Kai Fukami et al.
PHYSICAL REVIEW FLUIDS (2019)
Data-driven prediction of unsteady flow over a circular cylinder using deep learning
Sangseung Lee et al.
JOURNAL OF FLUID MECHANICS (2019)
Perspective on machine learning for advancing fluid mechanics
M. P. Brenner et al.
PHYSICAL REVIEW FLUIDS (2019)
Subgrid modelling for two-dimensional turbulence using neural networks
R. Maulik et al.
JOURNAL OF FLUID MECHANICS (2019)
Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework
Zhiwen Deng et al.
PHYSICS OF FLUIDS (2019)
Extreme learning machine for reduced order modeling of turbulent geophysical flows
Omer San et al.
PHYSICAL REVIEW E (2018)
Network structure of two-dimensional decaying isotropic turbulence
Kunihiko Taira et al.
JOURNAL OF FLUID MECHANICS (2016)
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling et al.
JOURNAL OF FLUID MECHANICS (2016)
A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions
Tim Colonius et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2008)
The immersed boundary method: A projection approach
Kunihiko Taira et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2007)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces
K Fukagata et al.
PHYSICS OF FLUIDS (2006)
分享论文
