Substructured Two-level and Multilevel Domain Decomposition Methods

08/15/2019 ∙ by Gabriele Ciaramella, et al. ∙ 0

Two-level domain decomposition methods are very powerful techniques for the efficient numerical solution of partial differential equations (PDEs). A two-level domain decomposition method requires two main components: a one-level preconditioner (or its corresponding smoothing iterative method), which is based on domain decomposition techniques, and a coarse correction step, which relies on a coarse space. The coarse space must properly represent the error components that the chosen one-level method is not capable to deal with. In the literature most of the works introduced efficient coarse spaces obtained as the span of functions defined on the entire space domain of the considered PDE. Therefore, the corresponding two-level preconditioners and iterative methods are defined in volume. In this paper, a new class of substructured two-level methods is introduced, for which both domain decomposition smoothers and coarse correction steps are defined on the interfaces. This approach has several advantages. On the one hand, the required computational effort is cheaper than the one required by classical volumetric two-level methods. On the other hand, it allows one to use some of the well-known efficient coarse spaces proposed in the literature. Moreover, our new substructured framework can be efficiently extended to a multi-level framework, which is always desirable when the high dimension or the scarce quality of the coarse space prevent the efficient numerical solution. Numerical experiments demonstrate the effectiveness of the proposed new numerical framework.



There are no comments yet.


page 31

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.