3 research outputs found

    The mixed virtual element discretization for highly-anisotropic problems: the role of the boundary degrees of freedom

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    In this paper, we discuss the accuracy and the robustness of the mixed Virtual Element Methods when dealing with highly anisotropic diffusion problems. In particular, we analyze the performance of different approaches which are characterized by different sets of both boundary and internal degrees of freedom in the presence of a strong anisotropy of the diffusion tensor with constant or variable coefficients. A new definition of the boundary degrees of freedom is also proposed and tested

    The lowest-order Neural Approximated Virtual Element Method

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    We introduce the Neural Approximated Virtual Element Method, a novel polygonal method that relies on neural networks to eliminate the need for projection and stabilization operators in the Virtual Element Method. In this paper, we discuss its formulation and detail the strategy for training the underlying neural network. The efficacy of this new method is tested through numerical experiments on elliptic problems

    Improving high-order VEM stability on badly-shaped elements

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    For the 2D and 3D Virtual Element Methods, a new approach to improve the conditioning of local and global matrices in the presence of badly-shaped polytopes is proposed. This new method defines the local projectors and the local degrees of freedom with respect to a set of scaled monomials recomputed on more well-shaped polytopes. This new approach is less computationally demanding than using the orthonormal polynomial basis. The effectiveness of our procedure is tested on different numerical examples characterized by challenging geometries of increasing complexity
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