A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate
By Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk. Computers Materials & Continua, 2021
503 citations recorded by the source corpus.
In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed. This method takes advantage of computational graphs and backpropagation algorithms involved in deep learning. Besides, the proposed DCM is based on a feedforward deep neural network (DNN) and differs from most previous applications of deep learning for mechanical problems. First, batches of randomly distributed collocation points are initially generated inside the domain and along the boundaries. A loss function is built with the aim that the governing partial differential equations (PDEs) of Kirchhoff plate bending problems, and the boundary/initial conditions are minimised at those collocation points. A combination of optimizers is adopted in the backpropagation process to minimize the loss function so as to obtain the optimal hyperparameters. In Kirchhoff plate bending problems, the C1 continuity requirement poses significant difficulties in traditional mesh-based methods. This can be solved by the proposed DCM, which uses a deep neural network to approximate the continuous transversal deflection, and is proved to be suitable to the bending analysis of Kirchhoff plate of various geometries.
Subfields: Civil and Structural Engineering, Mechanics of Materials, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/09b781e63c52dd7a85fec382cc1832db8232f48c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Xiaoying Zhuang
- Timon Rabczuk
- Naif Alajlan
- Hongwei Guo
- Stéphane P.A. Bordas
- Charles E. Augarde
- Shuwei Zhou
- Hong Zheng
In Mechanics of Materials
- Xiaoying Zhuang
- Timon Rabczuk
- Naif Alajlan
- Hongwei Guo
- Stéphane P.A. Bordas
- Charles E. Augarde
- Shuwei Zhou
- Trung Nguyen-Thoi
In Statistical and Nonlinear Physics
Citations in the corpus
- Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates (2020, methodology): “…methods, a variety of numerical methods have been developed including the finite element method [2, 3], boundary element method [4, 5], meshfree method [6, 7], isogeometric analysis (IGA) formulations [8], numerical manifold method [9, 10, 11] and recently deep learning based methods [12, 13, 14].”