664 research outputs found
Adaptive Low-Rank Methods for Problems on Sobolev Spaces with Error Control in
Low-rank tensor methods for the approximate solution of second-order elliptic
partial differential equations in high dimensions have recently attracted
significant attention. A critical issue is to rigorously bound the error of
such approximations, not with respect to a fixed finite dimensional discrete
background problem, but with respect to the exact solution of the continuous
problem. While the energy norm offers a natural error measure corresponding to
the underlying operator considered as an isomorphism from the energy space onto
its dual, this norm requires a careful treatment in its interplay with the
tensor structure of the problem. In this paper we build on our previous work on
energy norm-convergent subspace-based tensor schemes contriving, however, a
modified formulation which now enforces convergence only in . In order to
still be able to exploit the mapping properties of elliptic operators, a
crucial ingredient of our approach is the development and analysis of a
suitable asymmetric preconditioning scheme. We provide estimates for the
computational complexity of the resulting method in terms of the solution error
and study the practical performance of the scheme in numerical experiments. In
both regards, we find that controlling solution errors in this weaker norm
leads to substantial simplifications and to a reduction of the actual numerical
work required for a certain error tolerance.Comment: 26 pages, 7 figure
Adaptive Near-Optimal Rank Tensor Approximation for High-Dimensional Operator Equations
We consider a framework for the construction of iterative schemes for
operator equations that combine low-rank approximation in tensor formats and
adaptive approximation in a basis. Under fairly general assumptions, we obtain
a rigorous convergence analysis, where all parameters required for the
execution of the methods depend only on the underlying infinite-dimensional
problem, but not on a concrete discretization. Under certain assumptions on the
rates for the involved low-rank approximations and basis expansions, we can
also give bounds on the computational complexity of the iteration as a function
of the prescribed target error. Our theoretical findings are illustrated and
supported by computational experiments. These demonstrate that problems in very
high dimensions can be treated with controlled solution accuracy.Comment: 51 page
Legendre-Gauss-Lobatto grids and associated nested dyadic grids
Legendre-Gauss-Lobatto (LGL) grids play a pivotal role in nodal spectral
methods for the numerical solution of partial differential equations. They not
only provide efficient high-order quadrature rules, but give also rise to norm
equivalences that could eventually lead to efficient preconditioning techniques
in high-order methods. Unfortunately, a serious obstruction to fully exploiting
the potential of such concepts is the fact that LGL grids of different degree
are not nested. This affects, on the one hand, the choice and analysis of
suitable auxiliary spaces, when applying the auxiliary space method as a
principal preconditioning paradigm, and, on the other hand, the efficient
solution of the auxiliary problems. As a central remedy, we consider certain
nested hierarchies of dyadic grids of locally comparable mesh size, that are in
a certain sense properly associated with the LGL grids. Their actual
suitability requires a subtle analysis of such grids which, in turn, relies on
a number of refined properties of LGL grids. The central objective of this
paper is to derive just these properties. This requires first revisiting
properties of close relatives to LGL grids which are subsequently used to
develop a refined analysis of LGL grids. These results allow us then to derive
the relevant properties of the associated dyadic grids.Comment: 35 pages, 7 figures, 2 tables, 2 algorithms; Keywords:
Legendre-Gauss-Lobatto grid, dyadic grid, graded grid, nested grid
Double Greedy Algorithms: Reduced Basis Methods for Transport Dominated Problems
The central objective of this paper is to develop reduced basis methods for
parameter dependent transport dominated problems that are rigorously proven to
exhibit rate-optimal performance when compared with the Kolmogorov -widths
of the solution sets. The central ingredient is the construction of
computationally feasible "tight" surrogates which in turn are based on deriving
a suitable well-conditioned variational formulation for the parameter dependent
problem. The theoretical results are illustrated by numerical experiments for
convection-diffusion and pure transport equations. In particular, the latter
example sheds some light on the smoothness of the dependence of the solutions
on the parameters
Tensor-Sparsity of Solutions to High-Dimensional Elliptic Partial Differential Equations
A recurring theme in attempts to break the curse of dimensionality in the
numerical approximations of solutions to high-dimensional partial differential
equations (PDEs) is to employ some form of sparse tensor approximation.
Unfortunately, there are only a few results that quantify the possible
advantages of such an approach. This paper introduces a class of
functions, which can be written as a sum of rank-one tensors using a total of
at most parameters and then uses this notion of sparsity to prove a
regularity theorem for certain high-dimensional elliptic PDEs. It is shown,
among other results, that whenever the right-hand side of the elliptic PDE
can be approximated with a certain rate in the norm of
by elements of , then the solution can be
approximated in from to accuracy
for any . Since these results require
knowledge of the eigenbasis of the elliptic operator considered, we propose a
second "basis-free" model of tensor sparsity and prove a regularity theorem for
this second sparsity model as well. We then proceed to address the important
question of the extent such regularity theorems translate into results on
computational complexity. It is shown how this second model can be used to
derive computational algorithms with performance that breaks the curse of
dimensionality on certain model high-dimensional elliptic PDEs with
tensor-sparse data.Comment: 41 pages, 1 figur
Пути модернизации канального исследовательского реактора ИВГ1.М
Рассмотрены варианты перевода реактора ИВГ.1М на топливо пониженного обогащения. Приведены результаты оценок основных нейтронно-физических параметров активной зоны модернизированного реактора ИВГ.1М. Сделан вывод о том, что в качестве нового вида топлива могут выступать уран-цирконевые твэлы с повышенной концентрацией урана в сердечнике и с обогащением по 235U до 20 %
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