Laplacian graph based approach for uncertainty quantification of large scale dynamical systems

Arpan Mukherjee, Rahul Rai, Puneet Singla, Tarunraj Singh, Abani Patra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Design of nonlinear dynamic complex systems that are robust to uncertainties requires usage of uncertainty quantification methods. With a large number of states, quantifying uncertainty by conventional methods is computationally prohibitive. Conventional methods are also prone to error. When the number of interacting variables is large, it is prudent, if not imperative, to take advantage of special structural features of a decomposed system and come up with a substantial reduction in dimensionality to get a solution for analyzing the whole system. In this paper, we propose two new methods of state space decomposition of large-scale dynamical systems. The proposed methods not only take into consideration the initial values of the state variables but also the evolution of the trajectories of the states with time. The efficacy of the novel state space partitioning schemes on selected uncertainty quantification test problems are outlined. Initial results show that our state partitioning schemes are competitive or often better, compared to existing methods.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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