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Artificial neural network as an incremental non-linear constitutive model for a finite element code

机译:人工神经网络作为有限元代码的增量非线性本构模型

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摘要

A back propagation artificial neural network (BP ANN) is proposed as a tool for numerical modelling of the constitutive behaviour of a physically non-linear body. Training process of the ANN using experimental data is discussed in details and illustrated with an example. In particular, some difficulties in the constitutive description proposed in consistent, incremental form are discovered and two solutions are proposed to overcome them. Two numerical examples are presented. The first one deals with modelling of elasto-plastic hysteresis, the second shows the application of ANN to approximation of biaxial non-linear behaviour.
机译:提出使用反向传播人工神经网络(BP ANN)作为对物理非线性物体的本构行为进行数值建模的工具。使用实验数据对人工神经网络的训练过程进行了详细讨论,并举例说明。尤其是,在以一致的增量形式提出的本构描述中发现了一些困难,并提出了两种解决方案来克服它们。给出了两个数值示例。第一个涉及弹塑性滞后的建模,第二个显示了人工神经网络在双轴非线性行为近似中的应用。

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