Search Results for author:
Found 4 papers, 2 papers with code
Data-Driven Design for Metamaterials and Multiscale Systems: A Review
Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature.
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (~O(10^4 )) shape-only library has been prepared with no properties evaluated.
GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design.
Deep Generative Models for Geometric Design Under Uncertainty
Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization.
