Iterative algorithms for partitioned neural network approximation to partial differential equations

Hee Jun Yang; Hyea Hyun Kim

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arXiv · 2211.00225

Iterative algorithms for partitioned neural network approximation to partial differential equations

Hee Jun Yang·Hyea Hyun Kim

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Abstract

To enhance solution accuracy and training efficiency in neural network approximation to partial differential equations, partitioned neural networks can be used as a solution surrogate instead of a single large and deep neural network defined on the whole problem domain. In such a partitioned neural network approach, suitable interface conditions or subdomain boundary conditions are combined to obtain a convergent approximate solution. However, there has been no rigorous study on the convergence and parallel computing enhancement on the partitioned neural network approach. In this paper, iterative algorithms are proposed to address these issues. Our algorithms are based on classical additive Schwarz domain decomposition methods. Numerical results are included to show the performance of the proposed iterative algorithms.

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BibTeX RIS

Hee Jun Yang, Hyea Hyun Kim. 2022-11-01. Iterative algorithms for partitioned neural network approximation to partial differential equations. https://arxiv.org/abs/2211.00225

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