SIAM Undergraduate Research Online

Volume 18

In This Volume

  • DOI: 10.1137/24S1710498

    Authors

    Yutong Bu (Corresponding author – Emory University)

    Project Advisors

    Julianne Chung (Emory University)

    Abstract

    Solving large-scale Bayesian inverse problems presents significant challenges, particularly when the exact (discretized) forward operator is unavailable. These challenges often arise in image processing tasks due to unknown defects in the forward process that may result in varying degrees of inexactness in the forward model. Moreover, for many large-scale problems, computing the square root or inverse of the prior covariance matrix is infeasible such as when the covariance kernel is defined on irregular grids or is accessible only through matrix-vector products. This paper introduces an efficient approach by developing an inexact generalized Golub-Kahan decomposition that can incorporate varying degrees of inexactness in the forward model to solve large-scale generalized Tikhonov regularized problems. Further, a hybrid iterative projection scheme is developed to automatically select Tikhonov regularization parameters. Numerical experiments on simulated tomography reconstructions demonstrate the stability and effectiveness of this novel hybrid approach.

  • A Framework for Approximating Perturbed Optimal Control Problems

    Published electronically August 18, 2025
  • Generative Modeling with Diffusion

    Published electronically June 27, 2025
  • A Comprehensive Study of Covid-19 in Florida

    Published electronically March 12, 2025
  • Controlling Ball Progression in Soccer

    Published electronically February 20, 2025
  • A Laplace Equation on a Rectangle With Mixed Boundary Conditions

    Published electronically February 5, 2025
  • Modeling Traffic Conditions to Determine Shortest Path

    Published electronically January 10, 2025

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