Real-time design of architectural structures with differentiable mechanics and neural networks

TL;DR

This paper introduces a neural network-based method combined with differentiable mechanics to rapidly generate architecturally efficient designs that meet mechanical constraints, significantly speeding up the design process for structures like shells and towers.

Contribution

It presents a novel approach integrating neural networks with differentiable mechanics to ensure mechanical compliance and fast design generation, outperforming traditional methods in speed and accuracy.

Findings

Achieves real-time design generation with high accuracy.

Outperforms fully neural alternatives in generalization.

Enables integration into 3D modeling and physical fabrication.

Abstract

Designing mechanically efficient geometry for architectural structures like shells, towers, and bridges, is an expensive iterative process. Existing techniques for solving such inverse problems rely on traditional optimization methods, which are slow and computationally expensive, limiting iteration speed and design exploration. Neural networks would seem to offer a solution via data-driven amortized optimization, but they often require extensive fine-tuning and cannot ensure that important design criteria, such as mechanical integrity, are met. In this work, we combine neural networks with a differentiable mechanics simulator to develop a model that accelerates the solution of shape approximation problems for architectural structures represented as bar systems. This model explicitly guarantees compliance with mechanical constraints while generating designs that closely match target geometries. We validate our approach in two tasks, the design of masonry shells and cable-net towers. Our model achieves better accuracy and generalization than fully neural alternatives, and comparable accuracy to direct optimization but in real time, enabling fast and reliable design exploration. We further demonstrate its advantages by integrating it into 3D modeling software and fabricating a physical prototype. Our work opens up new opportunities for accelerated mechanical design enhanced by neural networks for the built environment.