most citedDeep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

physics.app-ph2022★ 2 cited

Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials

Helda Pahlavani, Kostas Tsifoutis-Kazolis, Prerak Mody +3

Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given…

cond-mat.mtrl-sci2022

Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials

H. Pahlavani, M. Amani, M. Cruz Saldívar +3

Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of mult…