Super-resolution generative adversarial networks of randomly-seeded fields
Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the mapping between …
spectrum of applications. This task is particularly challenging when the mapping between …
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 …
Flow reconstruction from sparse sensors based on reduced-order autoencoder state estimation
The reconstruction of accurate and robust unsteady flow fields from sparse and noisy data in
real-life engineering tasks is challenging, particularly when sensors are randomly placed. To …
real-life engineering tasks is challenging, particularly when sensors are randomly placed. To …
State estimation in minimal turbulent channel flow: A comparative study of 4DVar and PINN
The state of turbulent, minimal-channel flow is estimated from spatio-temporal sparse
observations of the velocity, using both a physics-informed neural network (PINN) and …
observations of the velocity, using both a physics-informed neural network (PINN) and …
Extraction and analysis of flow features in planar synthetic jets using different machine learning techniques
Synthetic jets are useful fluid devices with several industrial applications. In this study, we
use the flow fields generated by two synchronously operating synthetic jets and simulated …
use the flow fields generated by two synchronously operating synthetic jets and simulated …
Generative adversarial reduced order modelling
In this work, we present GAROM, a new approach for reduced order modeling (ROM) based
on generative adversarial networks (GANs). GANs attempt to learn to generate data with the …
on generative adversarial networks (GANs). GANs attempt to learn to generate data with the …
[HTML][HTML] Image features of a splashing drop on a solid surface extracted using a feedforward neural network
This article reports nonintuitive characteristic of a splashing drop on a solid surface
discovered through extracting image features using a feedforward neural network (FNN) …
discovered through extracting image features using a feedforward neural network (FNN) …
[HTML][HTML] Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models
Due to costs and practical constraints, field campaigns in the atmospheric boundary layer
typically only measure a fraction of the atmospheric volume of interest. Machine learning …
typically only measure a fraction of the atmospheric volume of interest. Machine learning …
Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks
Deep generative models (DGMs) have proven to be powerful in generating realistic data
samples. Their capability to learn the underlying distribution of a dataset enable them to …
samples. Their capability to learn the underlying distribution of a dataset enable them to …
Alternative auto-Encoder for state estimation in distribution systems with unobservability
P Sundaray, Y Weng - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The landscape of energy systems is ever changing due to the introduction of distributed
energy resources (DERs) on the generation side and new demand-response technologies …
energy resources (DERs) on the generation side and new demand-response technologies …