The ‘Atlas’ of Design Conceptual Space: A Design Thinking Framework with Cognitive and Computational Footings

Chen, Jielin; Stouffs, Rudi

doi:10.1007/978-3-031-20418-0_22

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Previous studies primarily focused on superior exploration strategies, leaving the intrinsic structure of design representation space vague [4]. As design activities are made possible because of designers' mental models of design representation spaces that designers constantly perceive and formulate [4]- [6], Chen & Stouffs [6], [8] promote two explicit models of design representation spaces: the sparse humanlearned model and the compressed machine-learned model, arguing that designers may enhance design performance by interacting with simulated design representation spaces. In this context, converting architectural design data into machineinterpretable formats is necessary, requiring flexible representation learning schemes.…”

Section: Architectural Design Representation Space Interpretationmentioning

confidence: 99%

From Exploration to Interpretation - Adopting Deep Representation Learning Models to Latent Space Interpretation of Architectural Design Alternatives

Chen¹,

Stouffs²

2021

CAADRIA Proceedings

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Despite the suitability of graphs for capturing the relational structures inherent in architectural layout designs, there is a notable dearth of research on interpreting architectural design space using graph-based representation learning and exploring architectural design graph generation. Concurrently, disentangled representation learning in graph generation faces challenges such as node permutation invariance and representation expressiveness. To address these challenges, we introduce an unsupervised disentangled representation learning framework, Style-based Edge-augmented Variational Graph Auto-Encoder (SE-VGAE), aiming to generate architectural layout in the form of attributed adjacency multi-graphs while prioritizing representation disentanglement. The framework is designed with three alternative pipelines, each integrating a transformer-based edgeaugmented encoder, a latent space disentanglement module, and a style-based decoder. These components collectively facilitate the decomposition of latent factors influencing architectural layout graph generation, enhancing generation fidelity and diversity. We also provide insights into optimizing the framework by systematically exploring graph feature augmentation schemes and evaluating their effectiveness for disentangling architectural layout representation through extensive experiments. Additionally, we contribute a new benchmark large-scale architectural layout graph dataset extracted from real-world floor plan images to facilitate the exploration of graph data-based architectural design representation space interpretation. This study pioneered disentangled representation learning for the architectural layout graph generation. The code and dataset of this study will be open-sourced.

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