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An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new Time-distributed Residual U-Net architecture

机译:Elastoplast Continua智能非线性元元素:利用新的时间分布式剩余U净架构的深度学习

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Substructuring is a model order reduction technique that accelerates the finite element method in solid mechanics. In this improved hybrid substructuring approach, methods from computational intelligence empower a reduced-order meta element. We propose a nonlinear and inelastic intelligent meta element for history-dependent boundary value problems. Fully compatible with conventional finite elements, it can be used to assemble larger structures. Within the intelligent meta element, a new deep neural network architecture composed of convolutions and recursions, the Time-distributed Residual U-Net (TRUNet), learns to solve the history-dependent spatial regression problem. The TRUNet automatically creates and updates the internal history variables necessary for the mechanical problem. Based on a new data generation strategy, data from a wide variety of use-cases train the neural network. An interface connects the neural network and the finite element method using a new data pre- and post-processing strategy. In three numerical demonstrations of elastoplastic continua, the intelligent meta element performs well, exhibiting low errors on a separate test dataset of several thousand samples. The intelligent reduced-order models compute considerably faster and achieve excellent approximations of the displacements, stresses, and forces. (C) 2020 Elsevier B.V. All rights reserved.
机译:子结构是一种模型顺序减少技术,其在固体力学中加速了有限元方法。在这种改进的混合子结构方法中,来自计算智能的方法赋予阶数元素。我们提出了一种非线性和非弹性智能元元素,用于历史依赖性边值问题。与传统的有限元完全兼容,它可用于组装较大的结构。在智能元元素中,一个由卷积和递归组成的新的深度神经网络架构,时间分布式的残差U-Net(Trunet),了解解决历史依赖的空间回归问题。 Trunet自动创建和更新机械问题所需的内部历史变量。基于新的数据生成策略,来自各种用例的数据是神经网络。接口使用新数据预和后处理策略连接神经网络和有限元方法。在弹性塑性连续体的三个数值演示中,智能元元素表现良好,在几千样本的单独测试数据集上表现出低误差。智能缩小级模型比较速度更快,实现了位移,应力和力的良好近似。 (c)2020 Elsevier B.v.保留所有权利。

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