6 citations · 6 across the 2 of their papers we have counts for
6 papers
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Matthew Lowery, John Turnage, Zachary Morrow +4
This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for…
Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
Zachary Morrow, Joseph Crockett, John D. Jakeman +1
Wildfires are a major producer of fine particulate matter, impacting human health and the electrical grid. Accurately forecasting smoke impacts over long time scales incorporates f…
An Optimal Weighted Least-Squares Method for Operator Learning
John Turnage, Matthew Lowery, John Jakeman +3
We consider the problem of learning an unknown, possibly nonlinear operator between separable Hilbert spaces from supervised data. Inputs are drawn from a prescribed probability me…
SUPN: Shallow Universal Polynomial Networks
Zachary Morrow, Michael Penwarden, Brian Chen +3
Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are popular methods for function approximation due to their flexibility and expressivity. However, they typically…
Toward real-time optimization through model reduction and model discrepancy sensitivities
Joseph Hart, Shane A. McQuarrie, Zachary Morrow +1
Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essen…
Glyph-Based Uncertainty Visualization and Analysis of Time-Varying Vector Fields
Timbwaoga A. J. Ouermi, Jixian Li, Zachary Morrow +2
Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance th…