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Research PaperResearchia:202605.14028

A multigrid and neural network approach to reduce the computational cost of phi-FEM

Raphaël Bulle

Abstract

In this work, we present a combination of a multigrid approach and the phi-FEM immersed boundary finite element method to reduce its computational cost while preserving its accuracy. To further reduce the numerical cost of the approach, we also propose the combination of the previous technique with some neural network methods. We illustrate the efficiency of these two approaches with numerical test cases in 2D and 3D. --- Source: arXiv:2605.13718v1 - http://arxiv.org/abs/2605.13718v1 PDF: https:...

Submitted: May 14, 2026Subjects: Mathematics; Mathematics

Description / Details

In this work, we present a combination of a multigrid approach and the phi-FEM immersed boundary finite element method to reduce its computational cost while preserving its accuracy. To further reduce the numerical cost of the approach, we also propose the combination of the previous technique with some neural network methods. We illustrate the efficiency of these two approaches with numerical test cases in 2D and 3D.


Source: arXiv:2605.13718v1 - http://arxiv.org/abs/2605.13718v1 PDF: https://arxiv.org/pdf/2605.13718v1 Original Link: http://arxiv.org/abs/2605.13718v1

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Date:
May 14, 2026
Topic:
Mathematics
Area:
Mathematics
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