Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems-Reference-Cited by-同舟云学术

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Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems

Published:2023-03-10 Issue:1 Volume:11 Page:285-319
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Alsup Terrence¹^ORCID,Peherstorfer Benjamin¹

Affiliation:

1. Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 USA.

Funder

Division of Mathematical Sciences

National Science Foundation

Air Force Office of Scientific 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

Reference42 articles.

1. Importance Sampling: Intrinsic Dimension and Computational Cost

2. Convergence rates for optimised adaptive importance samplers

3. Stochastic gradient optimization of importance sampling for the efficient simulation of digital communication systems

4. Approximation of Large-Scale Dynamical Systems

5. A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

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1. Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models;Advances in Computational Mathematics;2024-07-03

2. Learning Nonlinear Reduced Models from Data with Operator Inference;Annual Review of Fluid Mechanics;2024-01-19

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