Approximations of stochastic partial differential equations

Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations

We describe and analyze two numerical methods for a linear elliptic problem with stochastic coefficients and homogeneous Dirichlet boundary conditions. Here the aim of the computations is to approximate statistical moments of the solution, and, in particular, we give a priori error estimates for the computation of the expected value of the solution. The first method generates independent identi...

Stochastic Partial Differential Equations

Gaussian Process Approximations of Stochastic Differential Equations

Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modeling. Current solution methods are limited in their representation of the posterior process in the presence of data. In this work, we present a novel Gaussian process approximation to the posterior measure over paths for a general class of stochastic differential equations in the presen...

Difference Approximations to Partial Differential Equations

Applications of Stochastic Partial Differential Equations

s of the talks Robert Adler, Technion-Israel Institute of Technology, Israel On quantifying shape, with two applications to stochastic processes I shall discuss some classical Integral and Differential Geometric ways to classify shape, and describe 1. A new class of results about the excursion sets of smooth random fields which uses them. 2. An application of these classifiers to the study of t...