Paper Title
Numerical Solution Of Stochastic Differential Equations Using Moving Least Squares

Abstract
This paper investigates numerical solution of stochastic differential equations by applying moving least squares approximation in a stochastic Galerkin projection scheme. Two different types of probability distribution functions studied here are uniform and lognormal. Numerical solution of problems with uniform random inputs are compared with Legendre-base polynomial chaos expansion. In the case of lognormal distribution, (modified) Gram-Schmidt polynomial is used for comparison. In different examples it is demonstrated that the accuracy of moving least squares is comparable with the ones of Legendre-chaos expansion and Gram-Schmidt.