Selected Research
My research is centered on machine-learning-aided generative design,
with a special emphasis on energy efficiency and resilience.
My recent work concentrates on building stock analysis through
generative models and modeling urban environments with graph theory.
I am actively seeking collaboration opportunities in these areas.
|
|
MARL: Multi-scale Archetype Representation Learning
for Urban Building Energy Modeling
Xinwei Zhuang*,
Zixun Huang*,
Wentao Zeng,
Luisa Caldas,
ICCVW, 2023
paper
/
poster
/
github
Refined and open-source release of an automated, geometrically detailed,
localized building archetype generator for energy modeling.
This paper delves into the specifics of an algorithm designed to
automatically generate building archetypes tailored to local contexts,
with a focus on detailed geometric resolution for energy modeling purposes.
We provide all codes and data necessary for replication.
Zhuang X., Huang Z., Zeng W. and Caldas L. (2023)
MARL: Multi-scale Archetype Representation Learning for Urban Building Energy Modeling,
workshop on Computer Vision Aided Architectural Design (CVAAD),
at International Conference on Computer Vision (ICCV), Paris, France
|
|
Encoding Urban Ecologies: Automated Building Archetype Generation
through Self-Supervised Learning for Energy Modeling
Xinwei Zhuang*,
Zixun Huang*,
Wentao Zeng,
Luisa Caldas,
ACADIA, 2023
paper
/
video
Paper presents a high-level overview of the algorithm behind
the automated generation of localized building archetypes, outlines the algorithm's value proposition and its practical applications.
Zhuang X., Huang Z., Zeng W. and Caldas L. (2023) Encoding Urban
Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy
Modeling, in 2023 Conference on Association for Computer Aided Design in Architecture (ACADIA),
Denver, United States of America
|
|
Synthesis and Generation for 3D Architecture V
olume with Generative Modeling
Xinwei Zhuang,
Yi Ju,
Allen Yang,
Luisa Caldas,
International Journal of Architecture Computing, 2023
paper
Introduces 3D learning in the architectural domain, leveraging generative
models to understand and manipulate the complex morphology of buildings.
By constructing an original database specifically tailored for this purpose,
the research offers capabilities for high-dimensional design exploration
and energy performance evaluation.
Zhuang X., Ju Y., Yang, A. and Caldas L. (2023) Synthesis and Generation for
3D Architecture Volume with Generative Modeling, in International Journal of
Architecture Computing, Vol. 21, Issue 2: AI, Architecture, Accessibility, &
Data Justice, doi.10.1177/14780771231168233
|
|