A survey on generative adversarial networks: Variants, applications, and training
The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …
new and practical framework called Generative Adversarial Networks (GAN) due to their …
Unsupervised deep learning for super-resolution reconstruction of turbulence
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …
have used supervised learning, which requires paired data for training. This limitation …
[HTML][HTML] Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …
accessible due to limited computational or experimental resources. In many cases, fluid data …
[HTML][HTML] From coarse wall measurements to turbulent velocity fields through deep learning
This work evaluates the applicability of super-resolution generative adversarial networks
(SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse …
(SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse …
Fluid simulation on neural flow maps
We introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …
paradigm of implicit neural representations with fluid simulation based on the theory of flow …
Dl4scivis: A state-of-the-art survey on deep learning for scientific visualization
Since 2016, we have witnessed the tremendous growth of artificial intelligence+
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …
Generative modeling of turbulence
C Drygala, B Winhart, F di Mare, H Gottschalk - Physics of Fluids, 2022 - pubs.aip.org
We present a mathematically well-founded approach for the synthetic modeling of turbulent
flows using generative adversarial networks (GAN). Based on the analysis of chaotic …
flows using generative adversarial networks (GAN). Based on the analysis of chaotic …
Conditional generative adversarial network framework for airfoil inverse design
This paper describes the application of generative adversarial networks (GANs) to airfoil
inverse design. Specifically, this work focuses on creating new airfoil shapes via conditional …
inverse design. Specifically, this work focuses on creating new airfoil shapes via conditional …
A neural network multigrid solver for the Navier-Stokes equations
We present the deep neural network multigrid solver (DNN-MG) that we develop for the
instationary Navier-Stokes equations. DNN-MG improves computational efficiency using a …
instationary Navier-Stokes equations. DNN-MG improves computational efficiency using a …
Multi-fidelity generative deep learning turbulent flows
In computational fluid dynamics, there is an inevitable trade off between accuracy and
computational cost. In this work, a novel multi-fidelity deep generative model is introduced …
computational cost. In this work, a novel multi-fidelity deep generative model is introduced …