27 research outputs found
Regression-based variance reduction approach for strong approximation schemes
In this paper we present a novel approach towards variance reduction for
discretised diffusion processes. The proposed approach involves specially
constructed control variates and allows for a significant reduction in the
variance for the terminal functionals. In this way the complexity order of the
standard Monte Carlo algorithm () can be reduced down to
in case of the Euler
scheme with being the precision to be achieved. These theoretical
results are illustrated by several numerical examples.Comment: arXiv admin note: text overlap with arXiv:1510.0314
Almost sure stability of the Euler-Maruyama method with random variable stepsize for stochastic differential equations
In this paper, the Euler–Maruyama (EM) method with random variable stepsize is studied to reproduce the almost sure stability of the true solutions of stochastic differential equations. Since the choice of the time step is based on the current state of the solution, the time variable is proved to be a stopping time. Then the semimartingale convergence theory is employed to obtain the almost sure stability of the random variable stepsize EM solution. To our best knowledge, this is the first paper to apply the random variable stepsize (with clear proof of the stopping time) to the analysis of the almost sure stability of the EM method
Random field sampling for a simplified model of melt-blowing considering turbulent velocity fluctuations
In melt-blowing very thin liquid fiber jets are spun due to high-velocity air
streams. In literature there is a clear, unsolved discrepancy between the
measured and computed jet attenuation. In this paper we will verify numerically
that the turbulent velocity fluctuations causing a random aerodynamic drag on
the fiber jets -- that has been neglected so far -- are the crucial effect to
close this gap. For this purpose, we model the velocity fluctuations as vector
Gaussian random fields on top of a k-epsilon turbulence description and develop
an efficient sampling procedure. Taking advantage of the special covariance
structure the effort of the sampling is linear in the discretization and makes
the realization possible
Multilevel Monte Carlo methods
The author's presentation of multilevel Monte Carlo path simulation at the
MCQMC 2006 conference stimulated a lot of research into multilevel Monte Carlo
methods. This paper reviews the progress since then, emphasising the
simplicity, flexibility and generality of the multilevel Monte Carlo approach.
It also offers a few original ideas and suggests areas for future research