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The viscous flow of two immiscible fluids in a porous medium on the Darcy scale is governed by a system of nonlinear parabolic equations. If infinite mobility of one phase can be assumed (e.g. in soil layers in contact with the atmosphere) the system can be substituted by the scalar Richards model. Thus, the domain of the porous medium may be partitioned into disjoint subdomains with either the full two-phase or the simplified Richards model dynamics. Extending the one-model approach from [1, 2] we suggest coupling conditions for this hybrid model approach. Based on an Euler implicit discretisation, a linear iterative (-type) domain decomposition scheme is proposed, and proven to be convergent. The theoretical findings are verified by a comparative numerical study that in particular confirms the efficiency of the hybrid ansatz as compared to full two-phase model computations.
This paper proposes a deep-learning-based domain decomposition method (DeepDDM), which leverages deep neural networks (DNN) to discretize the subproblems divided by domain decomposition methods (DDM) for solving partial differential equations (PDE).
In ptychography experiments, redundant scanning is usually required to guarantee the stable recovery, such that a huge amount of frames are generated, and thus it poses a great demand of parallel computing in order to solve this large-scale inverse p
The discretization of certain integral equations, e.g., the first-kind Fredholm equation of Laplaces equation, leads to symmetric positive-definite linear systems, where the coefficient matrix is dense and often ill-conditioned. We introduce a new pr
This paper focuses on the fast evaluation of the matvec $g=Kf$ for $Kin mathbb{C}^{Ntimes N}$, which is the discretization of a multidimensional oscillatory integral transform $g(x) = int K(x,xi) f(xi)dxi$ with a kernel function $K(x,xi)=e^{2pii Phi(
In this paper, we propose a novel overlapping domain decomposition method that can be applied to various problems in variational imaging such as total variation minimization. Most of recent domain decomposition methods for total variation minimizatio