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Volume 43 Issue 3
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LAN Peng, LI Hai-chao, YE Xin-yu, ZHANG Sheng, SHENG Dai-chao. PINNs algorithm and its application in geotechnical engineering[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(3): 586-592. DOI: 10.11779/CJGE202103023
Citation: LAN Peng, LI Hai-chao, YE Xin-yu, ZHANG Sheng, SHENG Dai-chao. PINNs algorithm and its application in geotechnical engineering[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(3): 586-592. DOI: 10.11779/CJGE202103023
shu

PINNs algorithm and its application in geotechnical engineering

  • 1.

    School of Civil Engineering, Changsha Central South University, Changsha 400041, China

  • 2.

    School of Civil and Environmental Engineering, Sydney University of Technology, Sydney, NSW 2007, Australia

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  • Received Date: August 05, 2020
  • Available Online: December 04, 2022
    Graphical Abstract
    • Abstract
  • The physical information neural networks (PINNs) algorithm, a new mesh-free algorithm, uses the automatic differential method to embed the partial differential equation directly into the neural networks so as to realize the intelligent solution of the partial differential equation, which has the advantages of fast convergence speed and high computational accuracy. The PINNs algorithm has a promising application in geotechnical engineering because it can solve the complex partial differential equations (PDEs) and inverse the unknown parameters of the PDEs. In order to verify the feasibility of the PINNs algorithm in geotechnical engineering, the one-dimensional consolidation process with the continuous drainage boundary condition is taken as an example to illustrate the procedures of the PINNs algorithm in terms of both the forward and inverse problems. The results show that the PINNs solution is highly consistent with the analytical one, indicating that the PINNs algorithm can provide an alternative approach for solving the related problems in geotechnical engineering.
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    • References (16)
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