Applied Math Colloquium

Friday, October 11th, 2013, 11:30 AM
Cullimore Lecture Hall, Lecture Hall II
New Jersey Institute of Technology


An efficient model reduction algorithm for a class of stochastic configurations

Mahadevan Ganesh


Colorado School of Mines



We consider absorption and scattering of acoustic waves from uncertain stochastic configurations comprising multiple bodies with various material properties and develop tools to address the problem of quantifying uncertainties in the acoustic cross sections of the configurations. The uncertainty arises because, for example, the locations and orientations of the particles in the configurations are described through random variables, and statistical moments of the far-fields induced by the stochastic configurations facilitate quantification of the uncertainty. In this talk we discuss an efficient model reduction algorithm to simulate the statistical properties of the stochastic model. We demonstrate the efficiency of the algorithm for configurations with non-smooth and non-convex bodies with distinct material properties, and random locations and orientations with normal and log-normal distributions.