4.7 Article

A FETI approach to domain decomposition for meshfree discretizations of nonlocal problems

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2021.114148

关键词

Nonlocal models; Domain decomposition; Meshfree discretization; FETI

资金

  1. Sandia National Laboratories (SNL) Laboratory-directed Research and Development (LDRD) program [218318]
  2. U.S. Department of Energy, Office of Advanced Scientific Computing Research
  3. U.S. Department of Energy's National Nuclear Security Administration [DE-NA0003525]

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A domain decomposition method is proposed for efficient simulation of nonlocal problems, utilizing a multi-domain formulation with nonlocal interfaces. A distributed projected gradient algorithm is used to solve the Lagrange multiplier system, demonstrating high scalability and outperforming standard approaches.
We propose a domain decomposition method for the efficient simulation of nonlocal problems. Our approach is based on a multi-domain formulation of a nonlocal diffusion problem where the subdomains share nonlocal interfaces of the size of the nonlocal horizon. This system of nonlocal equations is first rewritten in terms of minimization of a nonlocal energy, then discretized with a meshfree approximation and finally solved via a Lagrange multiplier approach in a way that resembles the finite element tearing and interconnect method. Specifically, we propose a distributed projected gradient algorithm for the solution of the Lagrange multiplier system, whose unknowns determine the nonlocal interface conditions between subdomains. Several two-dimensional numerical tests on problems as large as 191 million unknowns illustrate the strong and the weak scalability of our algorithm, which outperforms the standard approach to the distributed numerical solution of the problem. This work is the first rigorous numerical study in a two-dimensional multi-domain setting for nonlocal operators with finite horizon and, as such, it is a fundamental step towards increasing the use of nonlocal models in large scale simulations. (C) 2021 Elsevier B.V. All rights reserved.

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