Machine Learning for Force Geometry: A Homology Model for Stress-Informed Shells

Abstract

As architecture faces rising demands for material efficiency, adaptability, and intelligent systems integration, new computational frameworks are needed to align performance with generative design. This paper presents a machine learning framework for predicting structurally meaningful lattice geometries in freeform architectural shells based on stress input. We introduce a Transformer-based model trained on scalar and directional stress fields to infer reinforcement patterns, producing polyhedral lattices aligned with principal stress trajectories. Operating without templates or rule-based encoding, the model generalizes across varied topologies and boundary conditions. By learning neighborhood relationships and stress flows, it internalizes structural logic beyond local cues, generating fabrication-ready outputs. Inference time is reduced from minutes to milliseconds, enabling a new scale of real-time structural reasoning. This approach bridges simulation and design, positioning AI as a tool for adaptive, performance-driven fabrication in architectural practice.