2 papers
cs.AI2026
RAG: A Random-Forest-Based Generative Design Framework for Uncertainty-Aware Design of Metamaterials with Complex Functional Response Requirements
Bolin Chen, Dex Doksoo Lee, Wei "Wayne'' Chen +1
Metamaterials design for advanced functionality often entails the inverse design on nonlinear and condition-dependent responses (e.g., stress-strain relation and dispersion relatio…
cs.LG2025
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data
Jiahui Zheng, Cole Jahnke, Wei "Wayne" Chen
This paper introduces GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning), a framework for quantifying free-form geometric uncertainties in…