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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…
cs.LG2024★ 1 cited
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
Shahroz Khan, Zahid Masood, Muhammad Usama +4
In this work, we propose a set of physics-informed geometric operators (GOs) to enrich the geometric data provided for training surrogate/discriminative models, dimension reduction…