activity
20142022
most citedPolynomial approximation via compressed sensing of high-dimensional functions on lower sets

120 citations · 245 across the 34 of their papers we have counts for

collaborators
Showing 2015Show all

7 papers · 1 filter

math.AP2015★ 6 cited

Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients

Hoang Tran, Clayton G. Webster, Guannan Zhang

In this work, we present a generalized methodology for analyzing the convergence of quasi-optimal Taylor and Legendre approximations, applicable to a wide class of parameterized el…

math.NA2015

A sparse grid method for Bayesian uncertainty quantification with application to large eddy simulation turbulence models

Hoang A. Tran, Clayton G. Webster, Guannan Zhang

There is wide agreement that the accuracy of turbulence models suffer from their sensitivity with respect to physical input data, the uncertainties of user-elected parameters, as w…

math.NA2015★ 4 cited

A Dynamically Adaptive Sparse Grid Method for Quasi-Optimal Interpolation of Multidimensional Analytic Functions

Miroslav K. Stoyanov, Clayton G. Webster

In this work we develop a dynamically adaptive sparse grids (SG) method for quasi-optimal interpolation of multidimensional analytic functions defined over a product of one dimensi…

math.NA2015

An Efficient Meshfreee Implicit Filter for Nonlinear Filtering Problems

Feng Bao, Yanzhao Cao, Clayton Webster +1

In this paper, we propose a meshfree approximation method for the implicit filter developed in [2], which is a novel numerical algorithm for nonlinear filtering problems. The impli…

math.NA2015★ 10 cited

Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients

Nick Dexter, Clayton Webster, Guannan Zhang

This work analyzes the overall computational complexity of the stochastic Galerkin finite element method (SGFEM) for approximating the solution of parameterized elliptic partial di…

math.NA2015★ 2 cited

Accelerating stochastic collocation methods for partial differential equations with random input data

Diego Galindo, Peter Jantsch, Clayton G. Webster +1

This work proposes and analyzes a generalized acceleration technique for decreasing the computational complexity of using stochastic collocation (SC) methods to solve partial diffe…