Nonlinear Order Reduction of Discretized Cell Population Models

Yongchun Zhang, Michael A. Henson, Yannis G. Kevrekidis

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Dynamic cell population balance models consist of nonlinear partial differential-integro equations. An accurate discretized approximation typically yields a large number of nonlinear ordinary differential equations which are not well suited for dynamic analysis and model-based controller design. In this paper, proper orthogonal decomposition is used to construct nonlinear reduced-order models from spatiotemporal data sets obtained via simulation of an accurate discretized cell population model. Dynamic simulation and bifurcation analysis results demonstrate that reduced-order models with a comparatively small number of differential equations yield accurate predictions over a wide range of operating conditions. We study the spectrum of the linearized model as a function of the level of discretization probing the existence of spectral gaps which typically lead to good model reduction.

Original languageAmerican English
Pages (from-to)2383-2388
Number of pages6
JournalProceedings of the American Control Conference
StatePublished - 2003
Event2003 American Control Conference - Denver, CO, United States
Duration: Jun 4 2003Jun 6 2003

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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