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arXiv 2102.02617math.NAcs.LGcs.NA
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Kirchhoff板弯曲分析的深层配置法

A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

Leibniz Universität Hannover(汉诺威莱布尼茨大学);Bauhaus-Universität Weimar(魏玛包豪斯大学);Tongji University(同济大学)

Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk

摘要:

本文提出了一种求解薄板弯曲问题的深层配置法。这种方法利用了深度学习中涉及的计算图和反向传播算法。此外,所提出的DCM基于前馈深度神经网络(DNN),与大多数以前的机械问题深度学习应用不同。首先,批量随机分布的配置点最初生成的域内和沿边界。以Kirchhoff板弯曲问题的控制偏微分方程(PDE)和边界条件/初始条件在配置点处最小为目标,建立了损失函数。在反向传播过程中采用优化器的组合,以最小化损失函数,从而获得最优的超参数。在Kirchhoff板弯曲问题中,C1连续性要求在传统的基于网格的方法中造成了很大的困难。这可以通过所提出的DCM来解决,该DCM使用深度神经网络来近似连续的横向挠度,并且被证明适用于各种几何形状的Kirchhoff板的弯曲分析。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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