14 citations · 14 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
Sparse -Autoencoders for Scientific Data Compression
Matthias Chung, Rick Archibald, Paul Atzberger +1
Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalid…
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems
Ryan Lopez, Paul J. Atzberger
We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mech…