PUMA publications for /group/simtech/partialThu May 18 11:32:12 CEST 2017{VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS}{COMPUTATIONAL ECONOMICS}{MAR}{3}{447-472}{A Non-stationary Model of Dividend Distribution in a Stochastic Interest-Rate Setting}{Article}{47}{2016}Finite methods control; for Numerical differential method} Singular equations; distribution; stochastic element {Dividend partial {In this paper the solutions to several variants of the so-called dividend-distribution problem in a multi-dimensional, diffusion setting are studied. In a nutshell, the manager of a firm must balance the retention of earnings (so as to ward off bankruptcy and earn interest) and the distribution of dividends (so as to please the shareholders). A dynamic-programming approach is used, where the state variables are the current levels of cash reserves and of the stochastic short-rate, as well as time. This results in a family of Hamilton-Jacobi-Bellman variational inequalities whose solutions must be approximated numerically. To do so, a finite element approximation and a time-marching scheme are employed.}Thu May 18 11:32:12 CEST 2017{3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA}{SIAM JOURNAL ON SCIENTIFIC COMPUTING}{4}{A2209-A2231}{UNCERTAINTY QUANTIFICATION FOR HYPERBOLIC CONSERVATION LAWS WITH FLUX COEFFICIENTS GIVEN BY SPATIOTEMPORAL RANDOM FIELDS}{Article}{38}{2016}hyperbolic field; field} finite Carlo quantification; Monte method; differential uncertainty random Ornstein-Uhlenbeck {stochastic volume flux equation; spatiotemporal Gaussian partial process; function; {In this paper hyperbolic partial differential equations (PDEs) with random coefficients are discussed. We consider the challenging problem of flux functions with coefficients modeled by spatiotemporal random fields. Those fields are given by correlated Gaussian random fields in space and Ornstein-Uhlenbeck processes in time. The resulting system of equations consists of a stochastic differential equation for each random parameter coupled to the hyperbolic conservation law. We de fine an appropriate solution concept in this setting and analyze errors and convergence of discretization methods. A novel discretization framework, based on Monte Carlo finite volume methods, is presented for the robust computation of moments of solutions to those random hyperbolic PDEs. We showcase the approach on two examples which appear in applications-the magnetic induction equation and linear acoustics both with a spatiotemporal random background velocity field.}