Convergence Rates for Learning Linear Operators from Noisy Data-Reference-Cited by-同舟云学术

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Convergence Rates for Learning Linear Operators from Noisy Data

Published:2023-05-11 Issue:2 Volume:11 Page:480-513
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

de Hoop Maarten V.¹,Kovachki Nikola B.²^ORCID,Nelsen Nicholas H.³^ORCID,Stuart Andrew M.³

Affiliation:

1. Simons Chair in Computational and Applied Mathematics and Earth Science, Rice University, Houston, TX 77005 USA.

2. NVIDIA AI, NVIDIA, Santa Clara, CA 95051 USA.

3. Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125 USA.

Funder

U.S. Department of Energy

Division of Mathematical Sciences

Division of Graduate Education

Air Force Office of Scientific Research

Division of Atmospheric and Geospace Sciences

Office of Naval Research

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference70 articles.

1. On statistical Calderón problems

2. Analysis of the Gibbs Sampler for Hierarchical Inverse Problems

3. Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems

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