NONLINEAR PARAMETRIC INVERSION USING INTERPOLATORY MODEL REDUCTION

doi:10.1137/130946320

Return

NONLINEAR PARAMETRIC INVERSION USING INTERPOLATORY MODEL REDUCTION

2015-01-01

De Sturler, Eric *

Serkan Gugercin

Kilmer, Misha E.

Chaturantabut, Saifon

Beattie, Christopher

O'Connell, Meghan

DOI：10.1137/130946320

Original

Save

View PDF

Abstract

En 中文

Nonlinear parametric inverse problems appear in several prominent applications; one such application is diffuse optical tomography (DOT) in medical image reconstruction. Such inverse problems present huge computational challenges, mostly due to the need for solving a sequence of large-scale discretized, parametrized partial differential equations in the forward model. In this paper, we show how interpolatory parametric model reduction can significantly reduce the cost of the inversion process in DOT by drastically reducing the computational cost of solving the forward problems. The key observation is that function evaluations for the underlying optimization problem may be viewed as transfer function evaluations along the imaginary axis; a similar observation holds for Jacobian evaluations as well. This motivates the use of system-theoretic model order reduction methods. We discuss the construction and use of interpolatory parametric reduced models as surrogates for the full forward model. Within the DOT setting, these surrogate models can approximate both the cost functional and the associated Jacobian with very little loss of accuracy while significantly reducing the cost of the overall inversion process. Four numerical examples illustrate the efficiency of the proposed approach.

Keywords:

DOT

PaLS

model reduction

rational interpolation

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.