Work

Stochastic Collocation on Unstructured Multivariate Meshes

Year: 2015

Type: article

Abstract: Collocation has become a standard tool for approximation of parameterized systems in the uncertainty quantification (UQ) community. Techniques for least-squares regularization, compressive sampling recovery, and interpolatory reconstruction are becoming standard tools used in a variety of applications. Selection of a collocation mesh is frequently a challenge, but methods that construct geometrically unstructured collocation meshes have shown great potential due to attractive theoretical properties and direct, simple generation and implementation. We investigate properties of these meshes, presenting stability and accuracy results that can be used as guides for generating stochastic collocation grids in multiple dimensions.

Source: Communications in Computational Physics

DOI: 10.4208/cicp.020215.070515a

Authors: Akil Narayan, Tao Zhou

Institutions: University of Utah, Chinese Academy of Sciences, Academy of Mathematics and Systems Science

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G6746421366, G757509706