Parametrized Model Order Reduction (PMOR): Sparse Data and Sparse Models

01 January 2009 → 31 December 2014
Research Foundation - Flanders (FWO)
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Computer architecture and networks
    • Distributed computing
    • Information sciences
    • Information systems
    • Programming languages
    • Scientific computing
    • Theoretical computer science
    • Visual computing
    • Other information and computing sciences
Parametrized Model Order Reduction (PMOR) macromodel linear time-invariant system (LTI)
Project description

The main objective of this project is the development of robust and stable rational modelling algorithms to build parametrized reduced order models for complex physical systems. The order and complexity of the scalable rational models are specifically tailored towards the application at hand. The approximation and/or interpolation models are based on sparse scatterded data,spread over the design space of interest, and the models are aimed at to be sparse themselves in order to guarantee a minimal complexity.