Reduced order fluid modeling with generative adversarial networks
Abstract: Surrogate models based on convolutional neural networks (CNNs) for computational fluid dynamics (CFD) simulations are investigated. In particular, the flow field inside two‐dimensional channels with a sudden expansion and an obstacle is predicted using an image representation of the geometry as the input. Generative adversarial neural networks (GANs) have been shown to excel at such image‐to‐image translation tasks. This motivates the focus of this work on investigating the specific effect of adversarial training on model performance. Numerical results show that the overall accuracy of the GANs is generally lower compared to an identical generator model trained directly on the ground truth using an L1 data loss. On the other hand, GAN predictions are often visually more convincing and exhibit a lower continuity residual.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Reduced order fluid modeling with generative adversarial networks ; volume:23 ; number:1 ; year:2023 ; extent:6
Proceedings in applied mathematics and mechanics ; 23, Heft 1 (2023) (gesamt 6)
- Urheber
-
Kemna, Mirko
Heinlein, Alexander
Vuik, Cornelis
- DOI
-
10.1002/pamm.202200241
- URN
-
urn:nbn:de:101:1-2023060115124297738393
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:44 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Kemna, Mirko
- Heinlein, Alexander
- Vuik, Cornelis
Ähnliche Objekte (12)
Generative Adversarial Privacy
Invisible steganography via generative adversarial networks
Generative adversarial nets for social scientists
Generative adversarial networks : Project relevant overview
Generative adversarial networks based skin lesion segmentation
Generating mobility networks with generative adversarial networks
Stacked generative adversarial networks for image compositing
Generative Adversarial Networks for Time Series Simulation
A quantum generative adversarial network for distributions
Large-Scale Training of Generative Adversarial Networks
Geophysical model generation with generative adversarial networks
Compressing PDF sets using generative adversarial networks
Generative Adversarial Privacy
Invisible steganography via generative adversarial networks
Generative adversarial nets for social scientists
Generative adversarial networks : Project relevant overview
Generative adversarial networks based skin lesion segmentation
Generating mobility networks with generative adversarial networks
Stacked generative adversarial networks for image compositing
Generative Adversarial Networks for Time Series Simulation
A quantum generative adversarial network for distributions
Large-Scale Training of Generative Adversarial Networks
Geophysical model generation with generative adversarial networks
Compressing PDF sets using generative adversarial networks
Generative Adversarial Privacy
Invisible steganography via generative adversarial networks
Generative adversarial nets for social scientists
Generative adversarial networks : Project relevant overview
Generative adversarial networks based skin lesion segmentation
Generating mobility networks with generative adversarial networks
Stacked generative adversarial networks for image compositing
Generative Adversarial Networks for Time Series Simulation
A quantum generative adversarial network for distributions
Large-Scale Training of Generative Adversarial Networks
Geophysical model generation with generative adversarial networks