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Neural network based constitutive modeling of nonlinear viscoplastic structural response

机译:基于神经网络的非线性粘塑结构应答的本构模拟

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In the present study constitutive equations in finite element simulations are replaced by means of an artificial neural network (ANN). Following this approach, a physically nonlinear stress-strain behavior with strain rate dependency is substituted by an algebraic system of equations. Implementing this mathematical approximation of a constitutive law into a finite element code, a so-called intelligent element is created. This approach leads to a significant reduction of computing time, because a complex material model is treated numerically by matrix multiplications as in the case of elasticity. Here, a viscoplastic material analysis by means of an ANN is proposed and applied to nonlinear structural behavior. (C) 2019 Elsevier Ltd. All rights reserved.
机译:在本研究中,通过人工神经网络(ANN)代替有限元模拟中的本质方程。 在这种方法之后,具有应变速率依赖性的物理上非线性应力 - 应变行为由等式的代数系统代替。 将本组则定律实施到有限元代码中,将该组成术的数学近似值实现为有限元代码,创建所谓的智能元素。 该方法导致计算时间的显着降低,因为在弹性的情况下,通过矩阵乘法来数值处理复杂的材料模型。 这里,提出借助于ANN的粘液材料分析并应用于非线性结构行为。 (c)2019年elestvier有限公司保留所有权利。

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