5 papers
Learning Generalizable Neural Operators for Inverse Problems
Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar +2
Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}…
Function Spaces Without Kernels: Learning Compact Hilbert Space Representations
Su Ann Low, Quentin Rommel, Kevin S. Miller +2
Function encoders are a recent technique that learn neural network basis functions to form compact, adaptive representations of Hilbert spaces of functions. We show that function e…
Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments
William Ward, Sarah Etter, Tyler Ingebrand +3
Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a cri…
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces
Tyler Ingebrand, Adam J. Thorpe, Ufuk Topcu
A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorith…
Basis-to-Basis Operator Learning Using Function Encoders
Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami +2
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We d…