Stochastic Modeling of Micro-Electromechanical Systems (Mems)

In the final part, a data-driven stochastic collocation approach is presented, which seeks to characterize uncertain input parameters based on available experimental information. This approach models the uncertain parameters as independent random variables, for which the distributions are estimated based on experimental observations, using a nonparametric diffusion mixing based estimator. The efficiency and applicability of the developed stochastic modeling framework is demonstrated by simulating several MEMS devices, such as MEMS switches, resonators, comb-drives etc.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Agarwal, Nitin

Contributors dc:contributor

• Aluru, Narayana R.

• Engineering, Mechanical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3391869

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/83935