220 research outputs found
A local hybrid surrogate-based finite element tearing interconnecting dual-primal method for nonsmooth random partial differential equations
A domain decomposition approach for high-dimensional random partial differential equations exploiting the localization of random parameters is presented. To obtain high efficiency, surrogate models in multielement representations in the parameter space are constructed locally when possible. The method makes use of a stochastic Galerkin finite element tearing interconnecting dual-primal formulation of the underlying problem with localized representations of involved input random fields. Each local parameter space associated to a subdomain is explored by a subdivision into regions where either the parametric surrogate accuracy can be trusted or where instead one has to resort to Monte Carlo. A heuristic adaptive algorithm carries out a problem-dependent hp-refinement in a stochastic multielement sense, anisotropically enlarging the trusted surrogate region as far as possible. This results in an efficient global parameter to solution sampling scheme making use of local parametric smoothness exploration for the surrogate construction. Adequately structured problems for this scheme occur naturally when uncertainties are defined on subdomains, for example, in a multiphysics setting, or when the KarhunenāLoĆØve expansion of a random field can be localized. The efficiency of the proposed hybrid technique is assessed with numerical benchmark problems illustrating the identification of trusted (possibly higher order) surrogate regions and nontrusted sampling regions. Ā© 2020 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons Ltd
A local hybrid surrogateābased finite element tearing interconnecting dualāprimal method for nonsmooth random partial differential equations
A domain decomposition approach for highādimensional random partial differential equations exploiting the localization of random parameters is presented. To obtain high efficiency, surrogate models in multielement representations in the parameter space are constructed locally when possible. The method makes use of a stochastic Galerkin finite element tearing interconnecting dualāprimal formulation of the underlying problem with localized representations of involved input random fields. Each local parameter space associated to a subdomain is explored by a subdivision into regions where either the parametric surrogate accuracy can be trusted or where instead one has to resort to Monte Carlo. A heuristic adaptive algorithm carries out a problemādependent hpārefinement in a stochastic multielement sense, anisotropically enlarging the trusted surrogate region as far as possible. This results in an efficient global parameter to solution sampling scheme making use of local parametric smoothness exploration for the surrogate construction. Adequately structured problems for this scheme occur naturally when uncertainties are defined on subdomains, for example, in a multiphysics setting, or when the KarhunenāLoĆØve expansion of a random field can be localized. The efficiency of the proposed hybrid technique is assessed with numerical benchmark problems illustrating the identification of trusted (possibly higher order) surrogate regions and nontrusted sampling regions
Block-adaptive Cross Approximation of Discrete Integral Operators
In this article we extend the adaptive cross approximation (ACA) method known
for the efficient approximation of discretisations of integral operators to a
block-adaptive version. While ACA is usually employed to assemble hierarchical
matrix approximations having the same prescribed accuracy on all blocks of the
partition, for the solution of linear systems it may be more efficient to adapt
the accuracy of each block to the actual error of the solution as some blocks
may be more important for the solution error than others. To this end, error
estimation techniques known from adaptive mesh refinement are applied to
automatically improve the block-wise matrix approximation. This allows to
interlace the assembling of the coefficient matrix with the iterative solution
H-matrix based second moment analysis for rough random fields and finite element discretizations
We consider the efficient solution of strongly elliptic partial differential equations with random load based on the finite element method. The solution's two-point correlation can efficiently be approximated by means of an H- matrix, in particular if the correlation length is rather short or the correlation kernel is nonsmooth. Since the inverses of the finite element matrices which correspond to the differential operator under consideration can likewise efficiently be approximated in the H- matrix format, we can solve the correspondent H- matrix equation in essentially linear time by using the H -matrix arithmetic. Numerical experiments for three-dimensional finite element discretizations for several correlation lengths and different smoothness are provided. They validate the presented method and demonstrate that the computation times do not increase for nonsmooth or shortly correlated data
Fast Numerical Methods for Non-local Operators
[no abstract available
Metric based up-scaling
We consider divergence form elliptic operators in dimension with
coefficients. Although solutions of these operators are only
H\"{o}lder continuous, we show that they are differentiable ()
with respect to harmonic coordinates. It follows that numerical homogenization
can be extended to situations where the medium has no ergodicity at small
scales and is characterized by a continuum of scales by transferring a new
metric in addition to traditional averaged (homogenized) quantities from
subgrid scales into computational scales and error bounds can be given. This
numerical homogenization method can also be used as a compression tool for
differential operators.Comment: Final version. Accepted for publication in Communications on Pure and
Applied Mathematics. Presented at CIMMS (March 2005), Socams 2005 (April),
Oberwolfach, MPI Leipzig (May 2005), CIRM (July 2005). Higher resolution
figures are available at http://www.acm.caltech.edu/~owhadi
Homogenization of Parabolic Equations with a Continuum of Space and Time Scales
This paper addresses the issue of the homogenization of linear divergence form parabolic operators in situations where no ergodicity and no scale separation in time or space are available. Namely, we consider divergence form linear parabolic operators in with -coefficients. It appears that the inverse operator maps the unit ball of into a space of functions which at small (time and space) scales are close in norm to a functional space of dimension . It follows that once one has solved these equations at least times it is possible to homogenize them both in space and in time, reducing the number of operation counts necessary to obtain further solutions. In practice we show under a Cordes-type condition that the first order time derivatives and second order space derivatives of the solution of these operators with respect to caloric coordinates are in (instead of with Euclidean coordinates). If the medium is time-independent, then it is sufficient to solve times the associated elliptic equation in order to homogenize the parabolic equation
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