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Fourier neural operator for real-time simulation of 3D dynamic urban microclimate — arXiv:2308.03985

Source: arXiv (en)
Original: https://arxiv.org/abs/2308.03985
License (as archived): CC0-1.0
Archived: 2026-10-08T14:10:55.819Z
SHA-256 of the archived text: 53d105152587a0077403411958fefa5bc25c91d4bf90ce32af0114a85a8ac9b3
Stable URL of this page: https://metraipolis.com/documento/dc_9505fffc215c

How to cite

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate — arXiv:2308.03985 — arXiv (en), https://arxiv.org/abs/2308.03985 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9505fffc215c (Metraipolis archive dc_9505fffc215c, sha256 53d105152587a0077403411958fefa5bc25c91d4bf90ce32af0114a85a8ac9b3)

BibTeX

@misc{dc9505fffc215c,
  title = {Fourier neural operator for real-time simulation of 3D dynamic urban microclimate — arXiv:2308.03985},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2308.03985},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9505fffc215c; sha256 53d105152587a0077403411958fefa5bc25c91d4bf90ce32af0114a85a8ac9b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2308.03985. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible.

Tema degli esploratori: terra, clima ed energia (elenco arXiv physics.ao-ph, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computational Engineering, Finance, and Science (cs.CE); Numerical Analysis (math.NA); Atmospheric and Oceanic Physics (physics.ao-ph); Fluid Dynamics (physics.flu-dyn).
Pagina del lavoro: https://arxiv.org/abs/2308.03985 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T13:24:45.511Z dalla pagina https://arxiv.org/list/physics.ao-ph/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).