132 research outputs found
A Dimension-Adaptive Multi-Index Monte Carlo Method Applied to a Model of a Heat Exchanger
We present an adaptive version of the Multi-Index Monte Carlo method,
introduced by Haji-Ali, Nobile and Tempone (2016), for simulating PDEs with
coefficients that are random fields. A classical technique for sampling from
these random fields is the Karhunen-Lo\`eve expansion. Our adaptive algorithm
is based on the adaptive algorithm used in sparse grid cubature as introduced
by Gerstner and Griebel (2003), and automatically chooses the number of terms
needed in this expansion, as well as the required spatial discretizations of
the PDE model. We apply the method to a simplified model of a heat exchanger
with random insulator material, where the stochastic characteristics are
modeled as a lognormal random field, and we show consistent computational
savings
Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
In this article, we propose a Milstein finite difference scheme for a
stochastic partial differential equation (SPDE) describing a large particle
system. We show, by means of Fourier analysis, that the discretisation on an
unbounded domain is convergent of first order in the timestep and second order
in the spatial grid size, and that the discretisation is stable with respect to
boundary data. Numerical experiments clearly indicate that the same convergence
order also holds for boundary-value problems. Multilevel path simulation,
previously used for SDEs, is shown to give substantial complexity gains
compared to a standard discretisation of the SPDE or direct simulation of the
particle system. We derive complexity bounds and illustrate the results by an
application to basket credit derivatives
Multilevel Methods for Uncertainty Quantification of Elliptic PDEs with Random Anisotropic Diffusion
We consider elliptic diffusion problems with a random anisotropic diffusion
coefficient, where, in a notable direction given by a random vector field, the
diffusion strength differs from the diffusion strength perpendicular to this
notable direction. The Karhunen-Lo\`eve expansion then yields a parametrisation
of the random vector field and, therefore, also of the solution of the elliptic
diffusion problem. We show that, given regularity of the elliptic diffusion
problem, the decay of the Karhunen-Lo\`eve expansion entirely determines the
regularity of the solution's dependence on the random parameter, also when
considering this higher spatial regularity. This result then implies that
multilevel collocation and multilevel quadrature methods may be used to lessen
the computation complexity when approximating quantities of interest, like the
solution's mean or its second moment, while still yielding the expected rates
of convergence. Numerical examples in three spatial dimensions are provided to
validate the presented theory
Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method
Computational tools for characterizing electromagnetic scattering from
objects with uncertain shapes are needed in various applications ranging from
remote sensing at microwave frequencies to Raman spectroscopy at optical
frequencies. Often, such computational tools use the Monte Carlo (MC) method to
sample a parametric space describing geometric uncertainties. For each sample,
which corresponds to a realization of the geometry, a deterministic
electromagnetic solver computes the scattered fields. However, for an accurate
statistical characterization the number of MC samples has to be large. In this
work, to address this challenge, the continuation multilevel Monte Carlo
(CMLMC) method is used together with a surface integral equation solver. The
CMLMC method optimally balances statistical errors due to sampling of the
parametric space, and numerical errors due to the discretization of the
geometry using a hierarchy of discretizations, from coarse to fine. The number
of realizations of finer discretizations can be kept low, with most samples
computed on coarser discretizations to minimize computational cost.
Consequently, the total execution time is significantly reduced, in comparison
to the standard MC scheme.Comment: 25 pages, 10 Figure
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