GUST combines synthetic-data pretraining with transfer learning on a conditional diffusion model to quantify free-form geometric uncertainty in manufactured metamaterials from small real-world datasets.
Wang, David Sell, Thaibao Phan, and Jonathan A
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data
GUST combines synthetic-data pretraining with transfer learning on a conditional diffusion model to quantify free-form geometric uncertainty in manufactured metamaterials from small real-world datasets.