A Unified Software Framework for Empirical Gramians
Himpe C, Ohlberger M
Research article (journal) | Peer reviewedAbstract
A common approach in model reduction is balanced truncation, which is based on Gramian matrices classifying certain attributes of states or parameters of a given dynamic system. Initially restricted to linear systems, the empirical Gramians not only extended this concept to nonlinear systems but also provided a uniform computational method. This work introduces a unified software framework supplying routines for six types of empirical Gramians. The Gramian types will be discussed and applied in a model reduction framework for multiple-input multiple-output systems.
Details about the publication
Volume: 2013
Issue: 2013
Page range: 1-6
Status: Published
Release year: 2013 (19/09/2013)
Language in which the publication is written: English
DOI: 10.1155/2013/365909
Keywords: Model Redcution; Parameter Identification; Sensitivity Analysis; Balanced Truncation; Controllability Gramian; Observability Gramian; Cross Gramian; Sensitivity Gramian; Identifiability Gramian; Joint Gramian
Authors from the University of Münster
| Himpe, Christian | |
| Ohlberger, Mario |
Projects the publication originates from
Duration: 01/11/2012 - 31/10/2019 | 1st Funding period Funded by: DFG - Cluster of Excellence Type of project: Subproject in DFG-joint project hosted at University of Münster |
Doctorates the publication originates from
| Combined State and Parameter Reduction for Nonlinear Systems with an Application in Neuroscience Candidate: Himpe, Christian | Supervisors: Ohlberger, Mario Period of time: until 20/06/2016 Doctoral examination procedure finished at: Doctoral examination procedure at University of Münster |