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Volume 17, Issue 2
Error Analysis of the Mixed Residual Method for Elliptic Equations

Kai Gu, Peng Fang, Zhiwei Sun & Rui Du

Numer. Math. Theor. Meth. Appl., 17 (2024), pp. 534-554.

Published online: 2024-05

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  • Abstract

We present a rigorous analysis of the convergence rate of the deep mixed residual method (MIM) when applied to a linear elliptic equation with different types of boundary conditions. The MIM has been proposed to solve high-order partial differential equations in high dimensions. Our analysis shows that MIM outperforms deep Ritz method and deep Galerkin method for weak solution in the Dirichlet case due to its ability to enforce the boundary condition. However, for the Neumann and Robin cases, MIM demonstrates similar performance to the other methods. Our results provide valuable insights into the strengths of MIM and its comparative performance in solving linear elliptic equations with different boundary conditions.

  • AMS Subject Headings

65N12, 65N15

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{NMTMA-17-534, author = {Gu , KaiFang , PengSun , Zhiwei and Du , Rui}, title = {Error Analysis of the Mixed Residual Method for Elliptic Equations}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2024}, volume = {17}, number = {2}, pages = {534--554}, abstract = {

We present a rigorous analysis of the convergence rate of the deep mixed residual method (MIM) when applied to a linear elliptic equation with different types of boundary conditions. The MIM has been proposed to solve high-order partial differential equations in high dimensions. Our analysis shows that MIM outperforms deep Ritz method and deep Galerkin method for weak solution in the Dirichlet case due to its ability to enforce the boundary condition. However, for the Neumann and Robin cases, MIM demonstrates similar performance to the other methods. Our results provide valuable insights into the strengths of MIM and its comparative performance in solving linear elliptic equations with different boundary conditions.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2023-0136}, url = {http://global-sci.org/intro/article_detail/nmtma/23111.html} }
TY - JOUR T1 - Error Analysis of the Mixed Residual Method for Elliptic Equations AU - Gu , Kai AU - Fang , Peng AU - Sun , Zhiwei AU - Du , Rui JO - Numerical Mathematics: Theory, Methods and Applications VL - 2 SP - 534 EP - 554 PY - 2024 DA - 2024/05 SN - 17 DO - http://doi.org/10.4208/nmtma.OA-2023-0136 UR - https://global-sci.org/intro/article_detail/nmtma/23111.html KW - Deep mixed residual method, deep neural network, error analysis, elliptic equations, Rademacher complexity AB -

We present a rigorous analysis of the convergence rate of the deep mixed residual method (MIM) when applied to a linear elliptic equation with different types of boundary conditions. The MIM has been proposed to solve high-order partial differential equations in high dimensions. Our analysis shows that MIM outperforms deep Ritz method and deep Galerkin method for weak solution in the Dirichlet case due to its ability to enforce the boundary condition. However, for the Neumann and Robin cases, MIM demonstrates similar performance to the other methods. Our results provide valuable insights into the strengths of MIM and its comparative performance in solving linear elliptic equations with different boundary conditions.

Gu , KaiFang , PengSun , Zhiwei and Du , Rui. (2024). Error Analysis of the Mixed Residual Method for Elliptic Equations. Numerical Mathematics: Theory, Methods and Applications. 17 (2). 534-554. doi:10.4208/nmtma.OA-2023-0136
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