Inverse designing surface curvatures by deep learning

TL;DR

This paper introduces a deep learning framework for the inverse design of microstructural topologies with specific surface curvature profiles, enabling targeted and diverse architectural features for bio-scaffolds and metamaterials.

Contribution

It presents a systematic deep learning approach to inverse design of microstructures based on surface curvature, surpassing ad hoc methods and enabling generalization to complex topologies.

Findings

Successfully designed topologies mimicking trabecular bone and spinodoids.

Demonstrated generalization beyond training data.

Linked curvature design to improved mechanical performance.

Abstract

Smooth and curved microstructural topologies found in nature - from soap films to trabecular bone - have inspired several mimetic design spaces for architected metamaterials and bio-scaffolds. However, the design approaches so far have been ad hoc, raising the challenge: how to systematically and efficiently inverse design such artificial microstructures with targeted topological features? Here, we explore surface curvature as a design modality and present a deep learning framework to produce topologies with as-desired curvature profiles. The inverse design framework can generalize to diverse topological features such as tubular, membranous, and particulate features. Moreover, we demonstrate successful generalization beyond both the design and data space by inverse designing topologies that mimic the curvature profile of trabecular bone, spinodoid topologies, and periodic nodal surfaces for application in bio-scaffolds and implants. Lastly, we bridge curvature and mechanics by showing how topological curvature can be designed to promote mechanically beneficial stretching-dominated deformation over bending-dominated deformation.