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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING Int. J. Numer. Meth. Engng 2010; 83:575597
 

Summary: INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
Int. J. Numer. Meth. Engng 2010; 83:575597
Published online 9 March 2010 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/nme.2844
A data-driven stochastic collocation approach for uncertainty
quantification in MEMS
Nitin Agarwal and N. R. Aluru,
Department of Mechanical Science and Engineering, Beckman Institute for Advanced Science and Technology,
University of Illinois at Urbana-Champaign, 405 N. Mathews Avenue, Urbana, IL 61801, U.S.A.
SUMMARY
This work presents a data-driven stochastic collocation approach to include the effect of uncertain design
parameters during complex multi-physics simulation of Micro-ElectroMechanical Systems (MEMS). The
proposed framework comprises of two key steps: first, probabilistic characterization of the input uncertain
parameters based on available experimental information, and second, propagation of these uncertainties
through the predictive model to relevant quantities of interest. The uncertain input parameters are modeled
as independent random variables, for which the distributions are estimated based on available experimental
observations, using a nonparametric diffusion-mixing-based estimator, Botev (Nonparametric density esti-
mation via diffusion mixing. Technical Report, 2007). The diffusion-based estimator derives from the
analogy between the kernel density estimation (KDE) procedure and the heat dissipation equation and
constructs density estimates that are smooth and asymptotically consistent. The diffusion model allows
for the incorporation of the prior density and leads to an improved density estimate, in comparison

  

Source: Aluru, Narayana R. - Department of Mechanical and Industrial Engineering, University of Illinois at Urbana-Champaign

 

Collections: Engineering; Materials Science