Convergence Rates for Learning Linear Operators from Noisy Data

Convergence Rates for Learning Linear Operators from Noisy Data

27 August 2021

Maarten V. de Hoop

Nikola B. Kovachki

Nicholas H. Nelsen

Andrew M. Stuart

Papers citing "Convergence Rates for Learning Linear Operators from Noisy Data"

17 / 17 papers shown

Title
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling Adrienne M. Propp Daniel M. Tartakovsky AI4CE 26 2 0 16 Oct 2024
Nonlinear functional regression by functional deep neural network with kernel embedding Zhongjie Shi Jun Fan Linhao Song Ding-Xuan Zhou Johan A. K. Suykens 50 5 0 05 Jan 2024
Inverse Problems with Learned Forward Operators Simon Arridge Andreas Hauptmann Yury Korolev 26 1 0 21 Nov 2023
Nonlocality and Nonlinearity Implies Universality in Operator Learning S. Lanthaler Zong-Yi Li Andrew M. Stuart 16 16 0 26 Apr 2023
Machine Learning for Partial Differential Equations Steven L. Brunton J. Nathan Kutz AI4CE 32 20 0 30 Mar 2023
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem Mattes Mollenhauer Nicole Mücke T. Sullivan 22 24 0 16 Nov 2022
Minimax Optimal Kernel Operator Learning via Multilevel Training Jikai Jin Yiping Lu Jose H. Blanchet Lexing Ying 18 11 0 28 Sep 2022
Approximation of Functionals by Neural Network without Curse of Dimensionality Yahong Yang Yang Xiang 15 6 0 28 May 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 30 7 0 15 May 2022
Learning Green's functions associated with time-dependent partial differential equations N. Boullé Seick Kim Tianyi Shi Alex Townsend AI4CE 21 25 0 27 Apr 2022
On the sample complexity of stabilizing linear dynamical systems from data Steffen W. R. Werner Benjamin Peherstorfer 17 7 0 28 Feb 2022
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu Haizhao Yang Minshuo Chen T. Zhao Wenjing Liao 32 36 0 01 Jan 2022
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks Thomas O'Leary-Roseberry Xiaosong Du A. Chaudhuri J. Martins Karen E. Willcox Omar Ghattas 30 22 0 14 Dec 2021
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces George Stepaniants 41 15 0 26 Aug 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 34 29 0 11 Dec 2020
A note on estimation in Hilbertian linear models Siegfried Hormann Łukasz Kidziński 66 34 0 14 Aug 2012
Conditional mean embeddings as regressors - supplementary Steffen Grunewalder Guy Lever Luca Baldassarre Sam Patterson A. Gretton Massimiliano Pontil 82 143 0 21 May 2012