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Form Finding Lab.
Princeton University

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Exhibit: Kirigami Strata – layers of symmetry and form at ECC, Venice, Italy

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This project combines kirigami engineering with artificial intelligence to reimagine the design of space frames for building components such as roofs, floors, and walls.
Kirigami is the art of cutting and arranging sheets to produce 3D objects. With kirigami principles, thin steel sheets can be cut, deployed, and connected to create stiff and easy-to-assemble space frames. A neural network equivariant to wallpaper group symmetries generates spinvalence cut patterns that endow a space frame with remarkable mechanical performance, distinctive architectural expression, and a mesmerizing interplay of light and shadow.
The exhibition is open until November 23rd at the mezanino level of Palazzo Mora, Venice, Italy. Keep up with our work at kirigami-strata.ai

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Publication: Digital guidework for augmented thin-tile vaulting construction

Masonry vaults are among the most elegant and efficient structural forms, yet their construction has long been slowed down by costly falsework and guidework.
Our team explored a new path: augmented reality as digital guidework. Instead of rigid scaffolding, masons can now follow holographic projections that provide just the right amount of visual support—keeping builders in control of their analog craft.
In field tests, this approach improved productivity by ~30% while achieving remarkable accuracy (within 1% of the vault span). Looking ahead, interactive mixed-reality could further boost precision, speed, and even training opportunities.
This work shows how centuries-old craftsmanship and cutting-edge technology can merge to keep masonry vaulting not just viable, but visionary. Read more about our findings here https://lnkd.in/d4-KcCxS

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Publication: Design of purely compressive shells under vertical and horizontal loads through Machine Learning-driven form-finding

We are excited to share our latest research on advancing the design of shell structures under challenging loading conditions. Traditionally, form-finding methods for masonry vaults focus on vertical loads, but extreme wind or seismic forces introduce significant design and safety complexities.
Our new paper explores an alternative to computationally intensive optimisation steps in Membrane Equilibrium Analysis. Using machine learning regression techniques—XGBoost, Random Forests, and k-Nearest Neighbours—we identify optimal Airy Stress Function parameters to improve efficiency and maintain structural integrity.
Case study results show that these methods can reduce computational demands while achieving material-efficient designs, with k-Nearest Neighbours delivering the best performance in our tests.
Read more about our findings here: https://lnkd.in/eEnu8sEW

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Exhibit: Alternative Skies at the Venice Architecture Biennale, Italy

We’re pleased to share that our latest paper, “Numerical modeling of cantilevered bigon arm mechanics under gravity,” by Axel Larsson @axla.io and Sigrid Adriaenssens is now published Open Access in the Journal of the Mechanics and Physics of Solids (link in bio)

In this work, we investigate the stability regimes of reconfigurable bigon arms under gravitational loading—offering new insights into multi-stable structural systems.

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