Enabling Hyper-Differential Sensitivity Analysis for Ill-Posed Inverse Problems
Author:
Affiliation:
1. Scientific Machine Learning, Sandia National Laboratories, Albuquerque, NM 87123-1320 USA.
Funder
National Nuclear Security Administration
U.S. Department of Energy
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Subject
Applied Mathematics,Computational Mathematics
Reference32 articles.
1. Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs
2. Frontiers in PDE-Constrained Optimization
3. Estimating nuisance parameters in inverse problems
4. Grid refinement and scaling for distributed parameter estimation problems
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