Abstract

We are interested in the uncertainty quantification of nonlinear hyperbolic equations. The aim is to evaluate the dependency of the solution to the uncertainty of the initial condition. I will present the stochastic finite volume method, and show how it can be adapted to take into account a large number of uncertain parameters using tensor-train approximation. I will introduce two different ways of using the tensor train format in this context, and present numerical results comparing the performances of both approaches. This work is a collaboration with Michael Herty and Siegfried Müller.