Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Application of improved physics-informed neural networks for nonlinear consolidation problems with continuous drainage boundary conditions
https://orcid.org/http://orcid.org/0000-0003-3558-6139
In this paper, improved physics-informed neural networks (PINNs) with hard constraints (PINNs-H) are introduced to simulate the variation of the excess pore water pressure in the nonlinear consolidation problems with continuous drainage boundary conditions. In the PINNs-H, we modify the network architecture to automatically satisfy the corresponding initial and boundary conditions accurately, and obtain high-precision soil consolidation behaviors. The accuracy and effectiveness of the presented PINNs-H are demonstrated on two examples of the nonlinear consolidation models. Specifically, the results indicate that based on less training data, we may better predict the consolidation behaviors through the PINNs-H. Furthermore, the training data required by the PINNs-H is significantly less than the grid point data of the finite difference method (FDM), and the PINNs-H exhibits a better memory advantage. For the inverse problem, we find that on the basis of less observed data of the excess pore water pressure, the PINNs can provide a great estimate to the interface parameters of the continuous drainage boundary conditions, and effectively resist the noise interference. We also use the PINNs and PINNs-H to identify the nonlinear factor, and reveal that PINNs-H can provide high-precision predicted results, whereas the PINNs fail.
Application of improved physics-informed neural networks for nonlinear consolidation problems with continuous drainage boundary conditions
https://orcid.org/http://orcid.org/0000-0003-3558-6139
In this paper, improved physics-informed neural networks (PINNs) with hard constraints (PINNs-H) are introduced to simulate the variation of the excess pore water pressure in the nonlinear consolidation problems with continuous drainage boundary conditions. In the PINNs-H, we modify the network architecture to automatically satisfy the corresponding initial and boundary conditions accurately, and obtain high-precision soil consolidation behaviors. The accuracy and effectiveness of the presented PINNs-H are demonstrated on two examples of the nonlinear consolidation models. Specifically, the results indicate that based on less training data, we may better predict the consolidation behaviors through the PINNs-H. Furthermore, the training data required by the PINNs-H is significantly less than the grid point data of the finite difference method (FDM), and the PINNs-H exhibits a better memory advantage. For the inverse problem, we find that on the basis of less observed data of the excess pore water pressure, the PINNs can provide a great estimate to the interface parameters of the continuous drainage boundary conditions, and effectively resist the noise interference. We also use the PINNs and PINNs-H to identify the nonlinear factor, and reveal that PINNs-H can provide high-precision predicted results, whereas the PINNs fail.
Application of improved physics-informed neural networks for nonlinear consolidation problems with continuous drainage boundary conditions
https://orcid.org/http://orcid.org/0000-0003-3558-6139
In this paper, improved physics-informed neural networks (PINNs) with hard constraints (PINNs-H) are introduced to simulate the variation of the excess pore water pressure in the nonlinear consolidation problems with continuous drainage boundary conditions. In the PINNs-H, we modify the network architecture to automatically satisfy the corresponding initial and boundary conditions accurately, and obtain high-precision soil consolidation behaviors. The accuracy and effectiveness of the presented PINNs-H are demonstrated on two examples of the nonlinear consolidation models. Specifically, the results indicate that based on less training data, we may better predict the consolidation behaviors through the PINNs-H. Furthermore, the training data required by the PINNs-H is significantly less than the grid point data of the finite difference method (FDM), and the PINNs-H exhibits a better memory advantage. For the inverse problem, we find that on the basis of less observed data of the excess pore water pressure, the PINNs can provide a great estimate to the interface parameters of the continuous drainage boundary conditions, and effectively resist the noise interference. We also use the PINNs and PINNs-H to identify the nonlinear factor, and reveal that PINNs-H can provide high-precision predicted results, whereas the PINNs fail.
Application of improved physics-informed neural networks for nonlinear consolidation problems with continuous drainage boundary conditions
Acta Geotech.
Lan, Peng (Autor:in) / Su, Jing-jing (Autor:in) / Ma, Xin-yan (Autor:in) / Zhang, Sheng (Autor:in)
Acta Geotechnica ; 19 ; 495-508
01.01.2024
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Continuous drainage boundary conditions , Hard constraints , Numerical solutions , Parameter inversion , Physics-informed neural network Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
| DOAJ | 2024
| Taylor & Francis Verlag | 2023
Application of physics-informed neural networks to inverse problems in unsaturated groundwater flow
| Taylor & Francis Verlag | 2022