On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise-Reference-Cited by-同舟云学术

On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise

Published:2021-01 Issue:1 Volume:9 Page:1-28
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Lu Shuai^ORCID,Niu Pingping,Werner Frank^ORCID

Funder

National Key Research and Development program of China

Deutsche Forschungsgemeinschaft

National Natural Science Foundation of China

Program of Shanghai Academic Research Leader

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

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

Link

https://epubs.siam.org/doi/pdf/10.1137/20M1330841

Reference33 articles.

1. An inverse random source problem for the Helmholtz equation

2. Iteratively Regularized Gauss–Newton Method for Nonlinear Inverse Problems with Random Noise

3. Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications

4. Optimal Adaptation for Early Stopping in Statistical Inverse Problems

5. Well posedness and convergence analysis of the ensemble Kalman inversion

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