Current and emerging deep-learning methods for the simulation of fluid dynamics
Over the last decade, deep learning (DL), a branch of machine learning, has experienced
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
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 …
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 …
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization
We focus on a convolutional neural network (CNN), which has recently been utilized for fluid
flow analyses, from the perspective on the influence of various operations inside it by …
flow analyses, from the perspective on the influence of various operations inside it by …
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 …
Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics
The simulation of fluid dynamics, typically by numerically solving partial differential
equations, is an essential tool in many areas of science and engineering. However, the high …
equations, is an essential tool in many areas of science and engineering. However, the high …
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
Recently, a growing number of studies have used machine learning (ML) models to
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …
Physics guided neural networks for modelling of non-linear dynamics
The success of the current wave of artificial intelligence can be partly attributed to deep
neural networks, which have proven to be very effective in learning complex patterns from …
neural networks, which have proven to be very effective in learning complex patterns from …
Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning
There is a growing interest in developing data-driven subgrid-scale (SGS) models for large-
eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some recent …
eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some recent …
[HTML][HTML] Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LESs) has
received substantial attention recently. Despite some success, particularly in a priori (offline) …
received substantial attention recently. Despite some success, particularly in a priori (offline) …