182 research outputs found
A geometric data structure for parallel finite elements and the application to multigrid methods with block smoothing
We present a parallel data structure which is directly linked to geometric quantities of an underlying mesh and which is well adapted to the requirements of a general finite element realization. In addition, we define an abstract linear algebra model which supports multigrid methods (extending our previous work in Comp. Vis. Sci. 1 (1997) 27-40). Finally, we apply the parallel multigrid preconditioner to several configurations in linear elasticity and we compute the condition number numerically for different smoothers, resulting in a quantitative evaluation of parallel multigrid performance
Efficient operator-coarsening multigrid schemes for local discontinuous Galerkin methods
An efficient -multigrid scheme is presented for local discontinuous
Galerkin (LDG) discretizations of elliptic problems, formulated around the idea
of separately coarsening the underlying discrete gradient and divergence
operators. We show that traditional multigrid coarsening of the primal
formulation leads to poor and suboptimal multigrid performance, whereas
coarsening of the flux formulation leads to optimal convergence and is
equivalent to a purely geometric multigrid method. The resulting
operator-coarsening schemes do not require the entire mesh hierarchy to be
explicitly built, thereby obviating the need to compute quadrature rules,
lifting operators, and other mesh-related quantities on coarse meshes. We show
that good multigrid convergence rates are achieved in a variety of numerical
tests on 2D and 3D uniform and adaptive Cartesian grids, as well as for curved
domains using implicitly defined meshes and for multi-phase elliptic interface
problems with complex geometry. Extension to non-LDG discretizations is briefly
discussed
Adaptive Discontinuous Galerkin Methods for Variational Inequalities with Applications to Phase Field Models
Solutions of variational inequalities often have limited regularity. In particular, the nonsmooth parts are local, while other parts of the solution have higher regularity. To overcome this limitation, we apply hp-adaptivity, which uses a combination of locally finer meshes and varying polynomial degrees to separate the different features of the the solution. For this, we employ Discontinuous Galerkin (DG) methods and show some novel error estimates for the obstacle problem which emphasize the use in hp-adaptive algorithms.
Besides this analysis, we present how to efficiently compute numerical solutions using error estimators, fast algebraic solvers which can also be employed in a parallel setup, and discuss implementation details.
Finally, some numerical examples and applications to phase field models are presented
Algebraic Multigrid for Markov Chains and Tensor Decomposition
The majority of this thesis is concerned with the development of efficient and robust numerical methods based on adaptive algebraic multigrid to compute the stationary distribution of Markov chains. It is shown that classical algebraic multigrid techniques can be applied in an exact interpolation scheme framework to compute the stationary distribution of irreducible, homogeneous Markov chains. A quantitative analysis shows that algebraically smooth multiplicative error is locally constant along strong connections in a scaled system operator, which suggests that classical algebraic multigrid coarsening and interpolation can be applied to the class of nonsymmetric irreducible singular M-matrices with zero column sums. Acceleration schemes based on fine-level iterant recombination, and over-correction of the coarse-grid correction are developed to improve the rate of convergence and scalability of simple adaptive aggregation multigrid methods for Markov chains. Numerical tests over a wide range of challenging nonsymmetric test problems demonstrate the effectiveness of the proposed multilevel method and the acceleration schemes.
This thesis also investigates the application of adaptive algebraic multigrid techniques for computing the canonical decomposition of higher-order tensors. The canonical decomposition is formulated as a least squares optimization problem, for which local minimizers are computed by solving the first-order optimality equations. The proposed multilevel method consists of two phases: an adaptive setup phase that uses a multiplicative correction scheme in conjunction with bootstrap algebraic multigrid interpolation to build the necessary operators on each level, and a solve phase that uses additive correction cycles based on the full approximation scheme to efficiently obtain an accurate solution. The alternating least squares method, which is a standard one-level iterative method for computing the canonical decomposition, is used as the relaxation scheme. Numerical tests show that for certain test problems arising from the discretization of high-dimensional partial differential equations on regular lattices the proposed multilevel method significantly outperforms the standard alternating least squares method when a high level of accuracy is required
CMB-S4 Science Book, First Edition
This book lays out the scientific goals to be addressed by the
next-generation ground-based cosmic microwave background experiment, CMB-S4,
envisioned to consist of dedicated telescopes at the South Pole, the high
Chilean Atacama plateau and possibly a northern hemisphere site, all equipped
with new superconducting cameras. CMB-S4 will dramatically advance cosmological
studies by crossing critical thresholds in the search for the B-mode
polarization signature of primordial gravitational waves, in the determination
of the number and masses of the neutrinos, in the search for evidence of new
light relics, in constraining the nature of dark energy, and in testing general
relativity on large scales
Local Fourier analysis for saddle-point problems
The numerical solution of saddle-point problems has attracted considerable interest in
recent years, due to their indefiniteness and often poor spectral properties that make
efficient solution difficult. While much research already exists, developing efficient
algorithms remains challenging. Researchers have applied finite-difference, finite element,
and finite-volume approaches successfully to discretize saddle-point problems,
and block preconditioners and monolithic multigrid methods have been proposed for
the resulting systems. However, there is still much to understand.
Magnetohydrodynamics (MHD) models the flow of a charged fluid, or plasma, in
the presence of electromagnetic fields. Often, the discretization and linearization of
MHD leads to a saddle-point system. We present vector-potential formulations of
MHD and a theoretical analysis of the existence and uniqueness of solutions of both
the continuum two-dimensional resistive MHD model and its discretization.
Local Fourier analysis (LFA) is a commonly used tool for the analysis of multigrid
and other multilevel algorithms. We first adapt LFA to analyse the properties of
multigrid methods for both finite-difference and finite-element discretizations of the
Stokes equations, leading to saddle-point systems. Monolithic multigrid methods,
based on distributive, Braess-Sarazin, and Uzawa relaxation are discussed. From
this LFA, optimal parameters are proposed for these multigrid solvers. Numerical
experiments are presented to validate our theoretical results. A modified two-level
LFA is proposed for high-order finite-element methods for the Lapalce problem, curing
the failure of classical LFA smoothing analysis in this setting and providing a reliable
way to estimate actual multigrid performance. Finally, we extend LFA to analyze the
balancing domain decomposition by constraints (BDDC) algorithm, using a new choice
of basis for the space of Fourier harmonics that greatly simplifies the application of
LFA. Improved performance is obtained for some two- and three-level variants
Application of domain decomposition methods to problems in topology optimisation
Determination of the optimal layout of structures can be seen in everyday life, from nature to industry, with research dating back to the eighteenth century. The focus of this thesis involves investigation into the relatively modern field of topology optimisation, where the aim is to determine both the optimal shape and topology of structures. However, the inherent large-scale nature means that even problems defined using a relatively coarse finite element discretisation can be computationally demanding.
This thesis aims to describe alternative approaches allowing for the practical use of topology optimisation on a large scale. Commonly used solution methods will be compared and scrutinised, with observations used in the application of a novel substructuring domain decomposition method for the subsequent large-scale linear systems. Numerical and analytical investigations involving the governing equations of linear elasticity will lead to the development of three different algorithms for compliance minimisation problems in topology optimisation. Each algorithm will involve an appropriate preconditioning strategy incorporating a matrix representation of a discrete interpolation norm, with numerical results indicating mesh independent performance
- …