3 papers
cs.LG2026
Extending Neural Operators: Robust Handling of Functions Beyond the Training Set
Blaine Quackenbush, Paul J. Atzberger
We develop a rigorous framework for extending neural operators to handle out-of-distribution input functions. We leverage kernel approximation techniques and provide theory for cha…
cs.LG2025
Transferable Foundation Models for Geometric Tasks on Point Cloud Representations: Geometric Neural Operators
Blaine Quackenbush, Paul J. Atzberger
We introduce methods for obtaining pretrained Geometric Neural Operators (GNPs) that can serve as basal foundation models for use in obtaining geometric features. These can be used…
cs.LG2024
Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators
Blaine Quackenbush, Paul J. Atzberger
We introduce Geometric Neural Operators (GNPs) for accounting for geometric contributions in data-driven deep learning of operators. We show how GNPs can be used (i) to estimate ge…