Patrick Janssen

Patrick Janssen
National University of Singapore | NUS · Department of Architecture

AA Dipl, PhD
mobius.design-automation.net

About

113
Publications
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719
Citations
Introduction
Patrick Janssen (江山)is an Associate Professor at the Department of Architecture at the National University of Singapore and is the Director of the Design Automation Laboratory. Patrick conducts research into computational methods and tools for design exploration and optimisation at building and urban scales. Websites: https://patrick.janssen.name, https://design-automation.net, https://mobius.design-automation.net
Additional affiliations
January 2016 - present
National University of Singapore
Position
  • Professor (Associate)
June 2008 - December 2015
National University of Singapore
Position
  • Professor (Assistant)

Network

Cited By

Projects

Projects (4)
Project
The aim of this project is to understand how the design search space can affect the result of parametric design optimisation for architecture, especially for the design problems related to building performance. To be more specific, how the shape (width, depth, and dense) of the design search space will pose difficulties and challenges for the parametric design optimisation process to find high-performing solutions, and can we modify the shape of the design search space to facilitate the parametric design optimisation process to produce more useful results.
Project
The aim of this project is to develop a computer-aided design approach for integrating building performance optimisation into concept architectural design exploration, by providing a more integrated building massing design generation and optimisation system that can produce site-specific and task-specific high-performing design solutions while with minimal interruption to architects' design processes. The system can help architects carry out performance-based design optimisation or optimisation-based design exploration in real-world design scenarios. In order to do so, the system enables architects to perform computational optimisation for performance-based building massing design without parametric modelling by providing two pre-defined building massing generative models (subtractive/additive models). In addition, the system also provides a hybrid evolutionary algorithm, named SSIEA, with enhancing design information feedback.