Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence-Reference-Cited by-同舟云学术

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Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

Published:2023-03-13 Issue:7 Volume:16 Page:1219-1238
ISSN:1996-3599
Container-title:Building Simulation
language:en
Short-container-title:Build. Simul.

Author:

Faulkner Cary A.,Jankowski Dominik S.,Castellini John E.,Zuo Wangda,Epple Philipp,Sohn Michael D.,Kasgari Ali Taleb Zadeh,Saad Walid

Publisher

Springer Science and Business Media LLC

Subject

Energy (miscellaneous),Building and Construction

Link

https://link.springer.com/content/pdf/10.1007/s12273-023-0989-1.pdf

Reference47 articles.

1. Albensoeder S, Kuhlmann HC (2005). Accurate three-dimensional lid-driven cavity flow. Journal of Computational Physics, 206: 536–558.

2. Antipov G, Baccouche M, Dugelay JL (2017). Face aging with conditional generative adversarial networks. In: Proceedings of 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.

3. Barth R, Hemming J, Van Henten EJ (2020). Optimising realism of synthetic images using cycle generative adversarial networks for improved part segmentation. Computers and Electronics in Agriculture, 173: 105378.

4. Blohm C, Kuhlmann HC (2002). The two-sided lid-driven cavity: Experiments on stationary and time-dependent flows. Journal of Fluid Mechanics, 450: 67–95.

5. Burggraf OR (1966). Analytical and numerical studies of the structure of steady separated flows. Journal of Fluid Mechanics, 24: 113–151.

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1. Rapid pedestrian‐level wind field prediction for early‐stage design using Pareto‐optimized convolutional neural networks;Computer-Aided Civil and Infrastructure Engineering;2024-04-25

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