An Assessment of Tool Interoperability and its Effect on Technological Uptake for Urban Microclimate Prediction with Deep Learning Models

Narridh Khean, Serjoscha Benjamin Düring, Angelos Chronis, Reinhard König, Matthias Hank Haeusler

Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband ohne Präsentation

Abstract

The benefits of deep learning (DL) models often overshadow the high costs associated with training them. Especially when the intention of the resultant model is a more climate resilient built environment, overlooking these costs are borderline hypocritical. However, the DL models that model natural phenomena— conventionally simulated through predictable mathematical modelling—don't succumb to the costly pitfalls of retraining when a model's predictions diverge from reality over time. Thus, the focus of this research will be on the application of DL models in urban microclimate simulations based on computational fluid dynamics. When applied, predicting wind factors through DL, rather than arduously simulating, can offer orders of magnitude of improved computational speed and costs. However, despite the plethora of research conducted on the training of such models, there is comparatively little work done on deploying them. This research posits: to truly use DL for climate resilience, it is not enough to simply train models, but also to deploy them in an environment conducive of rapid uptake with minimal barrier to entry. Thus, this research develops a Grasshopper plugin that offers planners and architects the benefits gained from DL. The outcomes of this research will be a tangible tool that practitioners can immediately use, toward making effectual change.
OriginalspracheEnglisch
TitelPOST-CARBON - Proceedings of the 27th CAADRIA Conference
Redakteure/-innenJeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth
Seiten273-282
Seitenumfang10
DOIs
PublikationsstatusVeröffentlicht - 2022

Research Field

  • Integrated Digital Urban Planning

Schlagwörter

  • Deep Learning; Technological Adoption; Fluid Dynamics; Urban Microclimate Simulation; Grasshopper; SDG 11

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