Maxine Perroni-Scharf’s research while affiliated with Princeton University and other places

Triply periodic minimal surfaces (TPMS) are a class of metamaterials with a variety of applications and well-known primitives. We present a new method for discovering novel microscale TPMS structures with exceptional energy-dissipation capabilities, achieving double the energy absorption of the best existing TPMS primitive structure. Our approach employs a parametric representation, allowing seamless interpolation between structures and representing a rich TPMS design space. We show that simulations are intractable for optimizing microscale hyperelastic structures, and instead propose a sample-efficient computational strategy for rapidly discovering structures with extreme energy dissipation using limited amounts of empirical data from 3D-printed and tested microscale metamaterials. This strategy ensures high-fidelity results but involves time-consuming 3D printing and testing. To address this, we leverage an uncertainty-aware Deep Ensembles model to predict microstructure behaviors and identify which structures to 3D-print and test next. We iteratively refine our model through batch Bayesian optimization, selecting structures for fabrication that maximize exploration of the performance space and exploitation of our energy-dissipation objective. Using our method, we produce the first open-source dataset of hyperelastic microscale TPMS structures, including a set of novel structures that demonstrate extreme energy dissipation capabilities. We show several potential applications of these structures in protective equipment and bone implants.

We present a method for the digital fabrication of surfaces whose appearance varies based on viewing direction. The surfaces are constructed from a mesh of bars arranged in a self-occluding colored heightfield that creates the desired view-dependent effects. At the heart of our method is a novel and simple differentiable rendering algorithm specifically designed to render colored 3D heightfields and enable efficient calculation of the gradient of appearance with respect to heights and colors. This algorithm forms the basis of a coarse-to-fine ML-based optimization process that adjusts the heights and colors of the strips to minimize the loss between the desired and real surface appearance from each viewpoint, deriving meshes that can then be fabricated using a 3D printer. Using our method, we demonstrate both synthetic and real-world fabricated results with view-dependent appearance.