Meshfree Variational Physics Informed Neural Networks (MF-VPINN): an adaptive training strategy

Stefano Berrone; Moreno Pintore

doi:10.3390/a17090415

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arXiv · 2406.19831

Meshfree Variational Physics Informed Neural Networks (MF-VPINN): an adaptive training strategy

Stefano Berrone·Moreno Pintore

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Abstract

In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the triangulation of the entire domain and that can be trained with an adaptive set of test functions. In order to generate the test space, we exploit an a posteriori error indicator and add test functions only where the error is higher. Four training strategies are proposed and compared. Numerical results show that the accuracy is higher than the one of a Variational-Physics-Informed Neural Network trained with the same number of test functions but defined on a quasi-uniform mesh.

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Stefano Berrone, Moreno Pintore. 2024-06-28. Meshfree Variational Physics Informed Neural Networks (MF-VPINN): an adaptive training strategy. https://doi.org/10.3390/a17090415

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