Super-resolution analysis via machine learning: a survey for fluid flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
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 …
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Achieving accurate and robust global situational awareness of a complex time-evolving field
from a limited number of sensors has been a long-standing challenge. This reconstruction …
from a limited number of sensors has been a long-standing challenge. This reconstruction …
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 …
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 …
Exploring the impact of Hall and ion slip effects on mixed convective flow of Casson fluid Model: A stochastic investigation through non-Fourier double diffusion …
Abstract The analysis of Non-Newtonian Casson fluid flow using Artificial Neural Networks
(ANNs) has enticed the attention of researchers and scientists due to their tremendous role …
(ANNs) has enticed the attention of researchers and scientists due to their tremendous role …
Experimental velocity data estimation for imperfect particle images using machine learning
We propose a method using supervised machine learning to estimate velocity fields from
particle images having missing regions due to experimental limitations. As a first example, a …
particle images having missing regions due to experimental limitations. As a first example, a …
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term cfd simulations via deep learning
While a big wave of artificial intelligence (AI) has propagated to the field of computational
fluid dynamics (CFD) acceleration studies, recent research has highlighted that the …
fluid dynamics (CFD) acceleration studies, recent research has highlighted that the …
Model order reduction with neural networks: Application to laminar and turbulent flows
We investigate the capability of neural network-based model order reduction, ie,
autoencoder (AE), for fluid flows. As an example model, an AE which comprises of …
autoencoder (AE), for fluid flows. As an example model, an AE which comprises of …