Special Session 139: New Developments in Computational Imaging, Learning, and Inverse Problems

Bi-level iterative regularization for inverse problems in nonlinear PDEs

Tram Nguyen
Max Planck Institute for Solar System Research
Germany
Co-Author(s):    
Abstract:
We investigate the ill-posed inverse problem of recovering unknown spatially dependent parameters in nonlinear evolution PDEs. We propose a bi-level Landweber scheme, where the upper-level parameter reconstruction embeds a lower-level state approximation. This can be seen as combining the classical reduced setting and the newer all-at-once setting, allowing us to, respectively, utilize well-posedness of the parameter-to-state map, and to bypass having to solve nonlinear PDEs exactly. Using this, we derive stopping rules for lower- and upper-level iterations and convergence of the bi-level method. We discuss application to parameter identification for the Landau-Lifshitz-Gilbert equation in magnetic particle imaging, as well as to several reaction-diffusion applications, in which the nonlinear reaction law needs to be determined.