Data-Driven Combined State and Parameter Reduction for Extreme-Scale Inverse Problems

Abstract

In this contribution we present an accelerated optimization-based approach for combined state and parameter reduction of a parametrized linear control system which is then used as a surrogate model in a Bayesian inverse setting. Following the basic ideas presented in [Lieberman, Willcox, Ghattas. Parameter and state model reduction for large-scale… (More)

Topics

5 Figures and Tables

Cite this paper

@inproceedings{Himpe2014DataDrivenCS, title={Data-Driven Combined State and Parameter Reduction for Extreme-Scale Inverse Problems}, author={Christian Himpe and Mario Ohlberger}, year={2014} }