2,028 research outputs found
Sparse spectral methods for integral equations and equilibrium measures
In this thesis, we introduce new numerical approaches to two important types of integral equation problems using sparse spectral methods. First, linear as well as nonlinear Volterra integral and integro-differential equations and second, power-law integral equations on d-dimensional balls involved in the solution of equilibrium measure problems.
These methods are based on ultraspherical spectral methods and share key properties and advantages as a result of their joint starting point: By working in appropriately weighted orthogonal Jacobi polynomial bases, we obtain recursively generated banded operators allowing us to obtain high precision solutions at low computational cost.
This thesis consists of three chapters in which the background of the above-mentioned problems and methods are respectively introduced in the context of their mathematical theory and applications, the necessary results to construct the operators and obtain solutions are proved and the method's applicability and efficiency are showcased by comparing them with current state-of-the-art approaches and analytic results where available. The first chapter gives a general scope introduction to sparse spectral methods using Jacobi polynomials in one and higher dimensions. The second chapter concerns the numerical solution of Volterra integral equations. The introduced method achieves exponential convergence and works for general kernels, a major advantage over comparable methods which are limited to convolution kernels.
The third chapter introduces an approximately banded method to solve power law kernel equilibrium measures in arbitrary dimensional balls. This choice of domain is suggested by the radial symmetry of the problem and analytic results on the supports of the resulting measures. For our method, we obtain the crucial property of computational cost independent of the dimension of the domain, a major contrast to particle simulations which are the current standard approach to these problems and scale extremely poorly with both the dimension and the number of particles.Open Acces
Computing the homology of basic semialgebraic sets in weak exponential time
We describe and analyze an algorithm for computing the homology (Betti
numbers and torsion coefficients) of basic semialgebraic sets which works in
weak exponential time. That is, out of a set of exponentially small measure in
the space of data the cost of the algorithm is exponential in the size of the
data. All algorithms previously proposed for this problem have a complexity
which is doubly exponential (and this is so for almost all data)
On p-Robust Saturation for hp-AFEM
We consider the standard adaptive finite element loop SOLVE, ESTIMATE, MARK,
REFINE, with ESTIMATE being implemented using the -robust equilibrated flux
estimator, and MARK being D\"orfler marking. As a refinement strategy we employ
-refinement. We investigate the question by which amount the local
polynomial degree on any marked patch has to be increase in order to achieve a
-independent error reduction. The resulting adaptive method can be turned
into an instance optimal -adaptive method by the addition of a coarsening
routine
Efficient spatial modelling using the SPDE approach with bivariate splines
Gaussian fields (GFs) are frequently used in spatial statistics for their
versatility. The associated computational cost can be a bottleneck, especially
in realistic applications. It has been shown that computational efficiency can
be gained by doing the computations using Gaussian Markov random fields (GMRFs)
as the GFs can be seen as weak solutions to corresponding stochastic partial
differential equations (SPDEs) using piecewise linear finite elements. We
introduce a new class of representations of GFs with bivariate splines instead
of finite elements. This allows an easier implementation of piecewise
polynomial representations of various degrees. It leads to GMRFs that can be
inferred efficiently and can be easily extended to non-stationary fields. The
solutions approximated with higher order bivariate splines converge faster,
hence the computational cost can be alleviated. Numerical simulations using
both real and simulated data also demonstrate that our framework increases the
flexibility and efficiency.Comment: 26 pages, 7 figures and 3 table
Error estimation and adaptive moment hierarchies for goal-oriented approximations of the Boltzmann equation
This paper presents an a-posteriori goal-oriented error analysis for a
numerical approximation of the steady Boltzmann equation based on a
moment-system approximation in velocity dependence and a discontinuous Galerkin
finite-element (DGFE) approximation in position dependence. We derive
computable error estimates and bounds for general target functionals of
solutions of the steady Boltzmann equation based on the DGFE moment
approximation. The a-posteriori error estimates and bounds are used to guide a
model adaptive algorithm for optimal approximations of the goal functional in
question. We present results for one-dimensional heat transfer and shock
structure problems where the moment model order is refined locally in space for
optimal approximation of the heat flux.Comment: arXiv admin note: text overlap with arXiv:1602.0131
- …