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The neural network collocation method for solving partial differential equations

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Abstract

This paper presents a meshfree collocation method that uses deep learning to determine the basis functions as well as their corresponding weights. This method is shown to be able to approximate elliptic, parabolic, and hyperbolic partial differential equations for both forced and unforced systems, as well as linear and nonlinear partial differential equations. By training a homogeneous network and particular network separately, new forcing functions are able to be approximated quickly without the burden of retraining the full network. The network is demonstrated on several numerical examples including a nonlinear elasticity problem. In addition to providing meshfree approximations to strong form partial differential equations directly, this technique could also provide a foundation for deep learning methods to be used as preconditioners to traditional methods, where the deep learning method will get close to a solution and traditional solvers can finish the solution.

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Acknowledgements

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the US Government.

Funding

This work was sponsored by Sandia National Laboratories’ Lab Directed Research and Development (LDRD) 2019 campaign.

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Authors and Affiliations

  1. Department of Structural Mechanics, Sandia National Laboratories, P.O. Box 5800, MS 0346, Albuquerque, NM, 57185-0346, USA

    Adam R. Brink

  2. Department of Component Sciences and Mechanics, ATA Engineering, Inc, 13290 Evening Creek Drive S, San Diego, CA, 92128, USA

    David A. Najera-Flores

  3. Department of Applied Machine Learning, Sandia National Laboratories, P.O. Box 5800, MS 0346, Albuquerque, NM, 57185-0346, USA

    Cari Martinez

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  1. Adam R. Brink
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Correspondence to Adam R. Brink.

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Brink, A.R., Najera-Flores, D.A. & Martinez, C. The neural network collocation method for solving partial differential equations. Neural Comput & Applic 33, 5591–5608 (2021). https://doi.org/10.1007/s00521-020-05340-5

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  • DOI: https://doi.org/10.1007/s00521-020-05340-5

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