1,344 research outputs found
Using cylindrical algebraic decomposition and local Fourier analysis to study numerical methods: two examples
Local Fourier analysis is a strong and well-established tool for analyzing
the convergence of numerical methods for partial differential equations. The
key idea of local Fourier analysis is to represent the occurring functions in
terms of a Fourier series and to use this representation to study certain
properties of the particular numerical method, like the convergence rate or an
error estimate.
In the process of applying a local Fourier analysis, it is typically
necessary to determine the supremum of a more or less complicated term with
respect to all frequencies and, potentially, other variables. The problem of
computing such a supremum can be rewritten as a quantifier elimination problem,
which can be solved with cylindrical algebraic decomposition, a well-known tool
from symbolic computation.
The combination of local Fourier analysis and cylindrical algebraic
decomposition is a machinery that can be applied to a wide class of problems.
In the present paper, we will discuss two examples. The first example is to
compute the convergence rate of a multigrid method. As second example we will
see that the machinery can also be used to do something rather different: We
will compare approximation error estimates for different kinds of
discretizations.Comment: The research was funded by the Austrian Science Fund (FWF): J3362-N2
Parametric free-form shape design with PDE models and reduced basis method
We present a coupling of the reduced basis methods and free-form deformations for shape optimization and design of systems modelled by elliptic PDEs. The free-form deformations give a parameterization of the shape that is independent of the mesh, the initial geometry, and the underlying PDE model. The resulting parametric PDEs are solved by reduced basis methods. An important role in our implementation is played by the recently proposed empirical interpolation method, which allows approximating the non-affinely parameterized deformations with affinely parameterized ones. These ingredients together give rise to an efficient online computational procedure for a repeated evaluation design environment like the one for shape optimization. The proposed approach is demonstrated on an airfoil inverse design problem. © 2010 Elsevier B.V
A Tool for Integer Homology Computation: Lambda-At Model
In this paper, we formalize the notion of lambda-AT-model (where is
a non-null integer) for a given chain complex, which allows the computation of
homological information in the integer domain avoiding using the Smith Normal
Form of the boundary matrices. We present an algorithm for computing such a
model, obtaining Betti numbers, the prime numbers p involved in the invariant
factors of the torsion subgroup of homology, the amount of invariant factors
that are a power of p and a set of representative cycles of generators of
homology mod p, for each p. Moreover, we establish the minimum valid lambda for
such a construction, what cuts down the computational costs related to the
torsion subgroup. The tools described here are useful to determine topological
information of nD structured objects such as simplicial, cubical or simploidal
complexes and are applicable to extract such an information from digital
pictures.Comment: Journal Image and Vision Computing, Volume 27 Issue 7, June, 200
Nested quasicrystalline discretisations of the line
One-dimensional cut-and-project point sets obtained from the square lattice
in the plane are considered from a unifying point of view and in the
perspective of aperiodic wavelet constructions. We successively examine their
geometrical aspects, combinatorial properties from the point of view of the
theory of languages, and self-similarity with algebraic scaling factor
. We explain the relation of the cut-and-project sets to non-standard
numeration systems based on . We finally examine the substitutivity, a
weakened version of substitution invariance, which provides us with an
algorithm for symbolic generation of cut-and-project sequences
Model-Based Problem Solving through Symbolic Regression via Pareto Genetic Programming.
Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust models by goal softening and adaptive fitness evaluations. In addition to the new strategies for model development and model selection, this dissertation presents a new approach for analysis, ranking, and compression of given multi-dimensional input-response data for the purpose of balancing the information content of undesigned data sets.
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