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 …
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
In the present study, we investigate different data-driven parameterizations for large eddy
simulation of two-dimensional turbulence in the a priori settings. These models utilize …
simulation of two-dimensional turbulence in the a priori settings. These models utilize …
Frame invariant neural network closures for Kraichnan turbulence
Numerical simulations of geophysical and atmospheric flows have to rely on
parameterizations of subgrid scale processes due to their limited spatial resolution. Despite …
parameterizations of subgrid scale processes due to their limited spatial resolution. Despite …
Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
In the past couple of years, there has been a proliferation in the use of machine learning
approaches to represent subgrid-scale processes in geophysical flows with an aim to …
approaches to represent subgrid-scale processes in geophysical flows with an aim to …
Large eddy simulation of flow over a circular cylinder with a neural-network-based subgrid-scale model
A neural-network-based large eddy simulation is performed for flow over a circular cylinder.
To predict the subgrid-scale (SGS) stresses, we train two fully connected neural network …
To predict the subgrid-scale (SGS) stresses, we train two fully connected neural network …
Modélisation du tenseur de contraintes sous-mailles par réseau de neurones à convolutions 3D en Turbulence homogène isotrope
N Saura - 2021 - theses.hal.science
La modélisation numérique de la turbulence est une des approches classiques pour étudier
la complexité de la dynamique des échelles actives dans un écoulement turbulent. La …
la complexité de la dynamique des échelles actives dans un écoulement turbulent. La …
Predicting unresolved scales interactions with 3D neural networks in homogeneous isotropic turbulence
N Saura, T Gomez - Europhysics Letters, 2023 - iopscience.iop.org
Interactions between the different scales of motion featured by any turbulent flow are
currently mathematically and numerically intractable. Instead, numerical reduced models …
currently mathematically and numerically intractable. Instead, numerical reduced models …
Subgrid stress tensor prediction in homogeneous isotropic turbulence using 3D-convolutional neural networks
N Saura, T Gomez - Available at SSRN 4184202 - papers.ssrn.com
Interactions between the different scales of motion featured by any turbulent flow are
currently mathematically and numerically intractable. Instead, numerical reduced models …
currently mathematically and numerically intractable. Instead, numerical reduced models …
Nonlinear System Discovery and Machine Learning for Dynamical Systems
SAS Romeo - 2023 - search.proquest.com
Extracting physics from data has become a crucial task in fields where abundant data is
available. However, the underlying governing equations, physical laws, or models based on …
available. However, the underlying governing equations, physical laws, or models based on …