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oai:www.ideals.illinois.edu:2142/83935 2023-07-11 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Aluru, Narayana R. Agarwal, Nitin 2015-09-25T21:12:46Z 2015-09-25T21:12:46Z 10000-01-01 2009 2009 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. Made available in DSpace on 2015-09-25T21:12:46Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3391869.pdf: 2889448 bytes, checksum: b455d78b7420a48e5d360f66c8792803 (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 85216 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 186 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/83935 (MiAaPQ)AAI3391869 eng Engineering, Mechanical Stochastic Modeling of Micro-Electromechanical Systems (Mems) text Mechanical Engineering Mechanical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.