Nanotech 2010 Vol. 2
Nanotech 2010 Vol. 2
Nanotechnology 2010: Electronics, Devices, Fabrication, MEMS, Fluidics and Computational

Computational Methods, Simulation & Software Tools Chapter 10

A Sparse Grid based Collocation Method for Model Order Reduction of Finite Element Models of MEMS under Uncertainty

Authors: P.S. Sumant, H. Wu, A.C. Cangellaris, N. Aluru

Affilation: University of Illinois at Urbana-Champaign, United States

Pages: 577 - 580

Keywords: model order reduction, finite element, stochastic, random input, krylov based

Abstract:
A methodology is proposed for the model order reduction of finite element models of MEMS devices under random input conditions. In this approach, the reduced order system matrices are represented in terms of their convergent orthogonal polynomial expansions of input random variables. The coefficients of these polynomials, which are matrices, are obtained by repeated, deterministic model order reduction of finite element models generated for specific values of the input random variables. These values are chosen efficiently in a multi-dimensional grid using a Smolyak algorithm. The stochastic reduced order model is represented in the form of an augmented system which can be used for generating the desired statistics of the specific system response. The proposed method provides for significant improvement in computational efficiency over standard Monte Carlo.

A Sparse Grid based Collocation Method for Model Order Reduction of Finite Element Models of MEMS under Uncertainty

ISBN: 978-1-4398-3402-2
Pages: 862
Hardcopy: $189.95