133 citations · 160 across the 8 of their papers we have counts for
4 papers · 1 filter
Enforcing Dirichlet Boundary Conditions in Operator Learning
Andrew M. Stuart, Margaret Trautner
Operator learning in scientific machine learning is concerned with approximation of maps between infinite-dimensional function spaces; such maps frequently arise as the solution op…
Energy-based Transport for Amortized Bayesian Inference
Ricardo Baptista, Hojjat Kaveh, Andrew M. Stuart
We consider amortized Bayesian inference for nonlinear inverse problems using only samples from the joint distribution of parameters and observations, including problems with unkno…
Discretization Error of Fourier Neural Operators
Samuel Lanthaler, Andrew M. Stuart, Margaret Trautner
Operator learning is a variant of machine learning that is designed to approximate maps between function spaces from data. The Fourier Neural Operator (FNO) is one of the main mode…
The Random Feature Model for Input-Output Maps between Banach Spaces
Nicholas H. Nelsen, Andrew M. Stuart
Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approxim…