Pedestrian wind flow prediction using spatial-frequency generative adversarial network-Reference-Cited by-同舟云学术

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

Published:2023-10-21 Issue:2 Volume:17 Page:319-334
ISSN:1996-3599
Container-title:Building Simulation
language:en
Short-container-title:Build. Simul.

Author:

Wang Pengyue,Guo Maozu,Cao Yingeng,Hao Shimeng,Zhou Xiaoping,Zhao Lingling

Publisher

Springer Science and Business Media LLC

Subject

Energy (miscellaneous),Building and Construction

Link

https://link.springer.com/content/pdf/10.1007/s12273-023-1071-8.pdf

Reference49 articles.

1. Balasubramanian A, Dhanasekaran H, Raghu B, et al. (2022). MRI super-resolution using generative adversarial network and discrete wavelet transform. In: Proceedings of 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS).

2. Baskaran A, Kashef A (1996). Investigation of air flow around buildings using computational fluid dynamics techniques. Engineering Structures, 18: 861–875.

3. Calzolari G, Liu W (2021). Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review. Building and Environment, 206: 108315.

4. Cao Q, Luan Q, Liu Y, et al. (2021). The effects of 2D and 3D building morphology on urban environments: A multi-scale analysis in the Beijing metropolitan region. Building and Environment, 192: 107635.

5. Chen Y, Li G, Jin C, et al. (2021). SSD-GAN: measuring the realness in the spatial and spectral domains. Proceedings of the AAAI Conference on Artificial Intelligence, 35: 1105–1112.

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1. Machine Learning for Pedestrian-Level Wind Comfort Analysis;Buildings;2024-06-18

2. Assessment on fast simulation of wind-driven pollutant dispersion around a street canyon with regime-switching Markov chain;Building and Environment;2024-06

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