Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140521/1/6.2015-2460.pd
This book presents methodologies for analysing large data sets produced by the direct numerical simu...
This dataset is used for the paper “Deep learning for subgrid-scale turbulence modeling in large-edd...
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various g...
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140498/1/6.2015-1287.pd
Turbulent convection flows are ubiquitous in natural systems such as in the atmosphere or in stellar...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The application of machine learning algorithms as data-driven turbulence modelling tools for Reynold...
• must estimate K(p,q) for distributions p and q • many useful kernels have a form that can be deriv...
The objective is to provide clear and well-motivated guidance to Machine Learning (ML) teams, founde...
Though turbulence is often thought to have universal behavior regardless of origin, it may be possib...
Accurate prediction of turbulent flows is important due to their typical key roles in engineering an...
Purpose: The paper aims to improve Reynolds-Averaged Navier Stokes (RANS) turbulence models using a ...
This book presents methodologies for analysing large data sets produced by the direct numerical simu...
This dataset is used for the paper “Deep learning for subgrid-scale turbulence modeling in large-edd...
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various g...
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140498/1/6.2015-1287.pd
Turbulent convection flows are ubiquitous in natural systems such as in the atmosphere or in stellar...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The problem of classifying turbulent environments from partial observation is key for some theoretic...
The application of machine learning algorithms as data-driven turbulence modelling tools for Reynold...
• must estimate K(p,q) for distributions p and q • many useful kernels have a form that can be deriv...
The objective is to provide clear and well-motivated guidance to Machine Learning (ML) teams, founde...
Though turbulence is often thought to have universal behavior regardless of origin, it may be possib...
Accurate prediction of turbulent flows is important due to their typical key roles in engineering an...
Purpose: The paper aims to improve Reynolds-Averaged Navier Stokes (RANS) turbulence models using a ...
This book presents methodologies for analysing large data sets produced by the direct numerical simu...
This dataset is used for the paper “Deep learning for subgrid-scale turbulence modeling in large-edd...
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various g...
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