Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES
We demonstrate how incorporating physics constraints into convolutional neural networks
(CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy …
(CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy …
[HTML][HTML] Recent developments in artificial intelligence in oceanography
C Dong, G Xu, G Han, BJ Bethel, W Xie… - Ocean-Land …, 2022 - spj.science.org
With the availability of petabytes of oceanographic observations and numerical model
simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of …
simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of …
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
In the last 50 years there has been a tremendous progress in solving numerically the Navier-
Stokes equations using finite differences, finite elements, spectral, and even meshless …
Stokes equations using finite differences, finite elements, spectral, and even meshless …
基于人工神经网络的湍流大涡模拟方法
谢晨月， 袁泽龙， 王建春， 万敏平， 陈十一 - 力学学报, 2021 - lxxb.cstam.org.cn
大涡模拟方法(LES) 是研究复杂湍流问题的重要工具, 在航空航天, 湍流燃烧, 气动声学,
大气边界层等众多工程领域中具有广泛的应用前景. 大涡模拟方法采用粗网格计算大尺度上的 …
大气边界层等众多工程领域中具有广泛的应用前景. 大涡模拟方法采用粗网格计算大尺度上的 …
Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network
The velocities measured by particle image velocimetry (PIV) and particle tracking
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
K Duraisamy - Physical Review Fluids, 2021 - APS
This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
A perspective on machine learning methods in turbulence modeling
This work presents a review of the current state of research in data‐driven turbulence
closure modeling. It offers a perspective on the challenges and open issues but also on the …
closure modeling. It offers a perspective on the challenges and open issues but also on the …
Toward neural-network-based large eddy simulation: Application to turbulent channel flow
A fully connected neural network (NN) is used to develop a subgrid-scale (SGS) model
mapping the relation between the SGS stresses and filtered flow variables in a turbulent …
mapping the relation between the SGS stresses and filtered flow variables in a turbulent …
[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
A posteriori learning for quasi‐geostrophic turbulence parametrization
The use of machine learning to build subgrid parametrizations for climate models is
receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised …
receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised …