678 research outputs found
An introduction to Multitrace Formulations and Associated Domain Decomposition Solvers
Multitrace formulations (MTFs) are based on a decomposition of the problem
domain into subdomains, and thus domain decomposition solvers are of interest.
The fully rigorous mathematical MTF can however be daunting for the
non-specialist. We introduce in this paper MTFs on a simple model problem using
concepts familiar to researchers in domain decomposition. This allows us to get
a new understanding of MTFs and a natural block Jacobi iteration, for which we
determine optimal relaxation parameters. We then show how iterative multitrace
formulation solvers are related to a well known domain decomposition method
called optimal Schwarz method: a method which used Dirichlet to Neumann maps in
the transmission condition. We finally show that the insight gained from the
simple model problem leads to remarkable identities for Calderon projectors and
related operators, and the convergence results and optimal choice of the
relaxation parameter we obtained is independent of the geometry, the space
dimension of the problem{\color{black}, and the precise form of the spatial
elliptic operator, like for optimal Schwarz methods. We illustrate our analysis
with numerical experiments
Nonlinear Preconditioning: How to use a Nonlinear Schwarz Method to Precondition Newton's Method
For linear problems, domain decomposition methods can be used directly as
iterative solvers, but also as preconditioners for Krylov methods. In practice,
Krylov acceleration is almost always used, since the Krylov method finds a much
better residual polynomial than the stationary iteration, and thus converges
much faster. We show in this paper that also for non-linear problems, domain
decomposition methods can either be used directly as iterative solvers, or one
can use them as preconditioners for Newton's method. For the concrete case of
the parallel Schwarz method, we show that we obtain a preconditioner we call
RASPEN (Restricted Additive Schwarz Preconditioned Exact Newton) which is
similar to ASPIN (Additive Schwarz Preconditioned Inexact Newton), but with all
components directly defined by the iterative method. This has the advantage
that RASPEN already converges when used as an iterative solver, in contrast to
ASPIN, and we thus get a substantially better preconditioner for Newton's
method. The iterative construction also allows us to naturally define a coarse
correction using the multigrid full approximation scheme, which leads to a
convergent two level non-linear iterative domain decomposition method and a two
level RASPEN non-linear preconditioner. We illustrate our findings with
numerical results on the Forchheimer equation and a non-linear diffusion
problem
Two-axis bend measurement with Bragg gratings in multicore optical fiber
We describe what is to our knowledge the first use of fiber Bragg gratings written into three separate cores of a multicore fiber for two-axis curvature measurement. The gratings act as independent, but isothermal, fiber strain gauges for which local curvature determines the difference in strain between cores, permitting temperature-independent bend measurement. (C) 2003 Optical Society of America
Linear and nonlinear substructured Restricted Additive Schwarz iterations and preconditioning
Iterative substructuring Domain Decomposition (DD) methods have been extensively studied, and they are usually associated with nonoverlapping decompositions. It is less known that classical overlapping DD methods can also be formulated in substructured form, i.e., as iterative methods acting on variables defined exclusively on the interfaces of the overlapping domain decomposition. We call such formulations substructured domain decomposition methods. We introduce here a substructured version of Restricted Additive Schwarz (RAS) which we call SRAS. We show that RAS and SRAS are equivalent when used as iterative solvers, as they produce the same iterates, while they are substantially different when used as preconditioners for GMRES. We link the volume and substructured Krylov spaces and show that the iterates are different by deriving the least squares problems solved at each GMRES iteration. When used as iterative solvers, SRAS presents computational advantages over RAS, as it avoids computations with matrices and vectors at the volume level. When used as preconditioners, SRAS has the further advantage of allowing GMRES to store smaller vectors and perform orthogonalization in a lower dimensional space. We then consider nonlinear problems, and we introduce SRASPEN (Substructured Restricted Additive Schwarz Preconditioned Exact Newton), where SRAS is used as a preconditioner for Newton’s method. In contrast to the linear case, we prove that Newton’s method applied to the preconditioned volume and substructured formulation produces the same iterates in the nonlinear case. Next, we introduce two-level versions of nonlinear SRAS and SRASPEN. Finally, we validate our theoretical results with numerical experiments
An introduction to multitrace formulations and associated domain decomposition solvers
Multi-trace formulations (MTFs) are based on a decomposition of the problem domain into subdomains, and thus domain decomposition solvers are of interest. The fully rigorous mathematical MTF can however be daunting for the non-specialist. The first aim of the present contribution is to provide a gentle introduction to MTFs. We introduce these formulations on a simple model problem using concepts familiar to researchers in domain decomposition. This allows us to get a new understanding of MTFs and a natural block Jacobi iteration, for which we determine optimal relaxation parameters. We then show how iterative multi-trace formulation solvers are related to a well known domain decomposition method called optimal Schwarz method: a method which used Dirichlet to Neumann maps in the transmission condition. We finally show that the insight gained from the simple model problem leads to remarkable identities for Calderón projectors and related operators, and the convergence results and optimal choice of the relaxation parameter we obtained is independent of the geometry, the space dimension of the problem, and the precise form of the spatial elliptic operator, like for optimal Schwarz methods. We illustrate our analysis with numerical experiments
Fourier Method for Approximating Eigenvalues of Indefinite Stekloff Operator
We introduce an efficient method for computing the Stekloff eigenvalues
associated with the Helmholtz equation. In general, this eigenvalue problem
requires solving the Helmholtz equation with Dirichlet and/or Neumann boundary
condition repeatedly. We propose solving the related constant coefficient
Helmholtz equation with Fast Fourier Transform (FFT) based on carefully
designed extensions and restrictions of the equation. The proposed Fourier
method, combined with proper eigensolver, results in an efficient and clear
approach for computing the Stekloff eigenvalues.Comment: 12 pages, 4 figure
Convergence of Parareal for the Navier-Stokes Equations Depending on the Reynolds Number
The paper presents first a linear stability analysis for the time-parallel Parareal method, using an IMEX Euler as coarse and a Runge-Kutta-3 method as fine propagator, confirming that dominant imaginary eigenvalues negatively affect Parareal’s convergence. This suggests that when Parareal is applied to the nonlinear Navier-Stokes equations, problems for small viscosities could arise. Numerical results for a driven cavity benchmark are presented, confirming that Parareal’s convergence can indeed deteriorate as viscosity decreases and the flow becomes increasingly dominated by convection. The effect is found to strongly depend on the spatial resolution
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