This study introduces an AI-assisted workflow for wind simulation in landscape form-finding. It can rapidly deliver a series of design options within designers' predefined constraints, each detailed with wind indicators. Integrating AI to detect subtle environmental changes and align with designers' intuitive decisions, this research fosters a collaborative paradigm between landscape architects and AI, aiming to shift from physics engine simulations to employing real-time AI simulations for rapidly aiding designers in the form-finding process in landscape design.
Autor / Author: | Tan, Chuheng; Zhong, Ximing; Fricker, Pia |
Institution / Institution: | Architectural Association School of Architecture, London/United Kingdom; Aalto University, Espoo/Finland; Aalto University, Espoo/Finland |
Seitenzahl / Pages: | 10 |
Sprache / Language: | Englisch |
Veröffentlichung / Publication: | JoDLA – Journal of Digital Landscape Architecture, 9-2024 |
Tagung / Conference: | Digital Landscape Architecture 2024 – New Trajectories in Computational Urban Landscapes and Ecology |
Veranstaltungsort, -datum / Venue, Date: | Vienna University of Technology, Austria 05-06-24 - 07-06-24 |
Schlüsselwörter (de): | |
Keywords (en): | Landscape form-finding, real-time wind-driven co-design, deep learning |
Paper review type: | Full Paper Review |
DOI: | doi:10.14627/537752008 |
Diese Website nutzt Cookies, um ihre Dienste anbieten zu können und Zugriffe zu analysieren. Dabei ist uns der Datenschutz sehr wichtig.
Legen Sie hier Ihre Cookie-Einstellungen fest. Sie können Sie jederzeit auf der Seite Cookie-Informationen ändern.