Automatic Generation of Reduced-Order Models for Linear Parametric Systems
Parametric modeling as well as parametric model order reduction (PMOR) of parametric systems are being widely researched in many micro- and nano-electrical(-mechanical) problems as well as in coupled micro- and nano-electro-thermal problems. We propose an adaptive technique for automatically implementing PMOR, so as to automatically construct the reduced-order models. The adaptive technique is based on a posteriori error estimation and is realized through a greedy algorithm which uses the error estimation as a stopping criteria.
KeywordsModel order reduction Multi-moment-matching Parametric model order reduction
This work is supported by the collaborative project nanoCOPS, Nanoelectronic COupled Problems Solutions, supported by the European Union in the FP7-ICT-2013-11 Program under Grant Agreement Number 619166.
- 3.Benner, P., Feng, L.: A robust algorithm for parametric model order reduction based on implicit moment-matching. In: Quarteroni, G.R.A. (ed.) Reduced Order Methods for Modeling and Computational Reduction. MS&A, vol. 9, pp. 159–186. Springer, Cham (2014)Google Scholar
- 5.Feng, L., Benner, P., Antoulas, A. C.: An a posteriori error bound for reduced order modeling of micro-and nano-electrical (-mechanical) systems. In: SCEE-2014 (Scientific Computing in Electrical Engineering), Wuppertal, Germany (2014)Google Scholar
- 6.Lefteriu, S., Antoulas, A.C., Ionita, A.C.: Parametric model reduction in the Loewner framework. In: Proceedings of 18th IFAC World Congress, pp. 12752–12756 (2011)Google Scholar
- 7.Patera, A.T., Rozza, G.: Reduced basis approximation and a posteriori error estimation for parametrized partial differential equations. MIT Pappalardo Graduate Monographs in Mechanical Engineering, Version 1.0, Copyright MIT 2006 (2007)Google Scholar