700 research outputs found
BPX preconditioners for the Bidomain model of electrocardiology
The aim of this work is to develop a BPX preconditioner for the Bidomain model of electrocardiology. This model describes the bioelectrical activity of the cardiac tissue and consists of a system of a non-linear parabolic reaction\u2013diffusion partial differential equation (PDE) and an elliptic linear PDE, modeling at macroscopic level the evolution of the transmembrane and extracellular electric potentials of the anisotropic cardiac tissue. The evolution equation is coupled through the non-linear reaction term with a stiff system of ordinary differential equations, the so-called membrane model, describing the ionic currents through the cellular membrane. The discretization of the coupled system by finite elements in space and semi-implicit finite differences in time yields at each time step the solution of an ill-conditioned linear system. The goal of the present study is to construct, analyze and numerically test a BPX preconditioner for the linear system arising from the discretization of the Bidomain model. Optimal convergence rate estimates are established and verified by two- and three-dimensional numerical tests on both structured and unstructured meshes. Moreover, in a full heartbeat simulation on a three-dimensional wedge of ventricular tissue, the BPX preconditioner is about 35% faster in terms of CPU times than ILU(0) and an Algebraic Multigrid preconditioner
Functional a posteriori error estimates for time-periodic parabolic optimal control problems
This paper is devoted to the a posteriori error analysis of multiharmonic
finite element approximations to distributed optimal control problems with
time-periodic state equations of parabolic type. We derive a posteriori
estimates of functional type, which are easily computable and provide
guaranteed upper bounds for the state and co-state errors as well as for the
cost functional. These theoretical results are confirmed by several numerical
tests that show high efficiency of the a posteriori error bounds
Robust Optimization of PDEs with Random Coefficients Using a Multilevel Monte Carlo Method
This paper addresses optimization problems constrained by partial
differential equations with uncertain coefficients. In particular, the robust
control problem and the average control problem are considered for a tracking
type cost functional with an additional penalty on the variance of the state.
The expressions for the gradient and Hessian corresponding to either problem
contain expected value operators. Due to the large number of uncertainties
considered in our model, we suggest to evaluate these expectations using a
multilevel Monte Carlo (MLMC) method. Under mild assumptions, it is shown that
this results in the gradient and Hessian corresponding to the MLMC estimator of
the original cost functional. Furthermore, we show that the use of certain
correlated samples yields a reduction in the total number of samples required.
Two optimization methods are investigated: the nonlinear conjugate gradient
method and the Newton method. For both, a specific algorithm is provided that
dynamically decides which and how many samples should be taken in each
iteration. The cost of the optimization up to some specified tolerance
is shown to be proportional to the cost of a gradient evaluation with requested
root mean square error . The algorithms are tested on a model elliptic
diffusion problem with lognormal diffusion coefficient. An additional nonlinear
term is also considered.Comment: This work was presented at the IMG 2016 conference (Dec 5 - Dec 9,
2016), at the Copper Mountain conference (Mar 26 - Mar 30, 2017), and at the
FrontUQ conference (Sept 5 - Sept 8, 2017
Generalizing Reduction-Based Algebraic Multigrid
Algebraic Multigrid (AMG) methods are often robust and effective solvers for
solving the large and sparse linear systems that arise from discretized PDEs
and other problems, relying on heuristic graph algorithms to achieve their
performance. Reduction-based AMG (AMGr) algorithms attempt to formalize these
heuristics by providing two-level convergence bounds that depend concretely on
properties of the partitioning of the given matrix into its fine- and
coarse-grid degrees of freedom. MacLachlan and Saad (SISC 2007) proved that the
AMGr method yields provably robust two-level convergence for symmetric and
positive-definite matrices that are diagonally dominant, with a convergence
factor bounded as a function of a coarsening parameter. However, when applying
AMGr algorithms to matrices that are not diagonally dominant, not only do the
convergence factor bounds not hold, but measured performance is notably
degraded. Here, we present modifications to the classical AMGr algorithm that
improve its performance on matrices that are not diagonally dominant, making
use of strength of connection, sparse approximate inverse (SPAI) techniques,
and interpolation truncation and rescaling, to improve robustness while
maintaining control of the algorithmic costs. We present numerical results
demonstrating the robustness of this approach for both classical isotropic
diffusion problems and for non-diagonally dominant systems coming from
anisotropic diffusion
Efficient and robust monolithic finite element multilevel Krylov subspace solvers for the solution of stationary incompressible Navier-Stokes equations
Multigrid methods belong to the best-known methods for solving linear systems
arising from the discretization of elliptic partial differential equations. The
main attraction of multigrid methods is that they have an asymptotically meshindependent
convergence behavior. Multigrid with Vanka (or local multilevel
pressure Schur complement method) as smoother have been frequently used for
the construction of very effcient coupled monolithic solvers for the solution of
the stationary incompressible Navier-Stokes equations in 2D and 3D. However,
due to its innate Gauß-Seidel/Jacobi character, Vanka has a strong influence
of the underlying mesh, and therefore, coupled multigrid solvers with Vanka
smoothing very frequently face convergence issues on meshes with high aspect
ratios. Moreover, even on very nice regular grids, these solvers may fail when
the anisotropies are introduced from the differential operator.
In this thesis, we develop a new class of robust and efficient monolithic finite
element multilevel Krylov subspace methods (MLKM) for the solution of the
stationary incompressible Navier-Stokes equations as an alternative to the coupled
multigrid-based solvers. Different from multigrid, the MLKM utilizes a
Krylov method as the basis in the error reduction process. The solver is based
on the multilevel projection-based method of Erlangga and Nabben, which accelerates
the convergence of the Krylov subspace methods by shifting the small
eigenvalues of the system matrix, responsible for the slow convergence of the
Krylov iteration, to the largest eigenvalue.
Before embarking on the Navier-Stokes equations, we first test our implementation
of the MLKM solver by solving scalar model problems, namely the
convection-diffusion problem and the anisotropic diffusion problem. We validate
the method by solving several standard benchmark problems. Next, we
present the numerical results for the solution of the incompressible Navier-Stokes
equations in two dimensions. The results show that the MLKM solvers produce
asymptotically mesh-size independent, as well as Reynolds number independent
convergence rates, for a moderate range of Reynolds numbers. Moreover, numerical
simulations also show that the coupled MLKM solvers can handle (both
mesh and operator based) anisotropies better than the coupled multigrid solvers
Spectral analysis of a block-triangular preconditioner for the bidomain system in electrocardiology
In this paper we analyze in detail the spectral properties of the block-triangular preconditioner introduced by Gerardo-Giorda et al. [J. Comput. Phys., 228 (2009), pp. 3625-3639] for the Bidomain system in non-symmetric form. We show that the conditioning of the preconditioned problem is bounded in the Fourier space independently of the frequency variable, ensuring quasi-optimality with respect to the mesh size. We derive an explicit formula to optimize the preconditioner performance by identifying a parameter that depends only on the coefficients of the problem and is easy to compute. We provide numerical tests in three dimensions that confirm the optimality of the parameter and the substantial independence of the mesh size. Copyrigh
Fast solution of Cahn-Hilliard variational inequalities using implicit time discretization and finite elements
We consider the e�cient solution of the Cahn-Hilliard variational inequality using an implicit time discretization, which is formulated as an optimal control problem with pointwise constraints on the control. By applying a semi-smooth Newton method combined with a Moreau-Yosida regularization technique for handling the control constraints we show superlinear convergence in function space. At the heart of this method lies the solution of large and sparse linear systems for which we propose the use of preconditioned Krylov subspace solvers using an e�ective Schur complement approximation. Numerical results illustrate the competitiveness of this approach
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