Text2Structure3D: Graph-Based Generative Modeling of Equilibrium Structures with Diffusion Transformers

TL;DR

Text2Structure3D is a novel graph-based generative model that creates equilibrium structural designs from natural language prompts, combining diffusion, graph auto-encoders, and optimization for accurate and generalizable results.

Contribution

It introduces a new ML framework that integrates diffusion, graph auto-encoders, and force optimization to generate equilibrium structures from text descriptions.

Findings

Generates structures closely matching text prompts.

Outperforms parametric models in generalization.

Ensures static equilibrium through post-processing.

Abstract

This paper presents Text2Structure3D, a graph-based Machine Learning (ML) model that generates equilibrium structures from natural language prompts. Text2Structure3D is designed to support new intuitive ways of design exploration and iteration in the conceptual structural design process. The approach combines latent diffusion with a Variational Graph Auto-Encoder (VGAE) and graph transformers to generate structural graphs that are close to an equilibrium state. Text2Structure3D integrates a residual force optimization post-processing step that ensures generated structures fully satisfy static equilibrium. The model was trained and validated using a cross-typological dataset of funicular form-found and statically determinate bridge structures, paired with text descriptions that capture the formal and structural features of each bridge. Results demonstrate that Text2Structure3D generates equilibrium structures with strong adherence to text-based specifications and greatly improves generalization capabilities compared to parametric model-based approaches. Text2Structure3D represents an early step toward a general-purpose foundation model for structural design, enabling the integration of generative AI into conceptual design workflows.