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Deep Learning Method Based on Physics-Informed Neural Network for 3D Anisotropic Steady-State Heat Conduction Problems

Author

Listed:
  • Zebin Xing

    (School of Applied Science, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Heng Cheng

    (School of Applied Science, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Jing Cheng

    (College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, China)

Abstract

This paper uses the physical information neural network (PINN) model to solve a 3D anisotropic steady-state heat conduction problem based on deep learning techniques. The model embeds the problem’s governing equations and boundary conditions into the neural network and treats the neural network’s output as the numerical solution of the partial differential equation. Then, the network is trained using the Adam optimizer on the training set. The output progressively converges toward the accurate solution of the equation. In the first numerical example, we demonstrate the convergence of the PINN by discussing the effect of the neural network’s number of layers, each hidden layer’s number of neurons, the initial learning rate and decay rate, the size of the training set, the mini-batch size, the amount of training points on the boundary, and the training steps on the relative error of the numerical solution, respectively. The numerical solutions are presented for three different examples. Thus, the effectiveness of the method is verified.

Suggested Citation

  • Zebin Xing & Heng Cheng & Jing Cheng, 2023. "Deep Learning Method Based on Physics-Informed Neural Network for 3D Anisotropic Steady-State Heat Conduction Problems," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4049-:d:1246624
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    References listed on IDEAS

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    1. Jing Cheng & Xiaowei Luo, 2022. "Analyzing the Land Leasing Behavior of the Government of Beijing, China, via the Multinomial Logit Model," Land, MDPI, vol. 11(3), pages 1-14, March.
    2. Ying Li & Longxiang Xu & Shihui Ying, 2022. "DWNN: Deep Wavelet Neural Network for Solving Partial Differential Equations," Mathematics, MDPI, vol. 10(12), pages 1-35, June.
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