3,768 research outputs found
Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo
In this work we develop a new hierarchical multilevel approach to generate
Gaussian random field realizations in an algorithmically scalable manner that
is well-suited to incorporate into multilevel Markov chain Monte Carlo (MCMC)
algorithms. This approach builds off of other partial differential equation
(PDE) approaches for generating Gaussian random field realizations; in
particular, a single field realization may be formed by solving a
reaction-diffusion PDE with a spatial white noise source function as the
righthand side. While these approaches have been explored to accelerate forward
uncertainty quantification tasks, e.g. multilevel Monte Carlo, the previous
constructions are not directly applicable to multilevel MCMC frameworks which
build fine scale random fields in a hierarchical fashion from coarse scale
random fields. Our new hierarchical multilevel method relies on a hierarchical
decomposition of the white noise source function in which allows us to
form Gaussian random field realizations across multiple levels of
discretization in a way that fits into multilevel MCMC algorithmic frameworks.
After presenting our main theoretical results and numerical scaling results to
showcase the utility of this new hierarchical PDE method for generating
Gaussian random field realizations, this method is tested on a four-level MCMC
algorithm to explore its feasibility
Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes
When solving stochastic partial differential equations (SPDEs) driven by
additive spatial white noise, the efficient sampling of white noise
realizations can be challenging. Here, we present a new sampling technique that
can be used to efficiently compute white noise samples in a finite element
method and multilevel Monte Carlo (MLMC) setting. The key idea is to exploit
the finite element matrix assembly procedure and factorize each local mass
matrix independently, hence avoiding the factorization of a large matrix.
Moreover, in a MLMC framework, the white noise samples must be coupled between
subsequent levels. We show how our technique can be used to enforce this
coupling even in the case of non-nested mesh hierarchies. We demonstrate the
efficacy of our method with numerical experiments. We observe optimal
convergence rates for the finite element solution of the elliptic SPDEs of
interest in 2D and 3D and we show convergence of the sampled field covariances.
In a MLMC setting, a good coupling is enforced and the telescoping sum is
respected.Comment: 28 pages, 10 figure
Stochastic turbulence modeling in RANS simulations via Multilevel Monte Carlo
A multilevel Monte Carlo (MLMC) method for quantifying model-form
uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS)
simulations is presented. Two, high-dimensional, stochastic extensions of the
RANS equations are considered to demonstrate the applicability of the MLMC
method. The first approach is based on global perturbation of the baseline eddy
viscosity field using a lognormal random field. A more general second extension
is considered based on the work of [Xiao et al.(2017)], where the entire
Reynolds Stress Tensor (RST) is perturbed while maintaining realizability. For
two fundamental flows, we show that the MLMC method based on a hierarchy of
meshes is asymptotically faster than plain Monte Carlo. Additionally, we
demonstrate that for some flows an optimal multilevel estimator can be obtained
for which the cost scales with the same order as a single CFD solve on the
finest grid level.Comment: 40 page
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