Although being a popular approach for the modeling of laminated composites, mesoscale constitutive models often struggle to represent material response for arbitrary load cases. A better alternative in terms of accuracy is to use the FE² technique to upscale microscopic material behavior without loss of generality, but the associated computational effort can be extreme. It is therefore interesting to explore alternative surrogate modeling strategies that maintain as much of the fidelity of FE² as possible while still being computationally efficient. In this work, three surrogate modeling approaches are compared in terms of accuracy, efficiency and calibration effort: the state-of-the-art mesoscopic plasticity model by Vogler et al. (Vogler et al., 2013), regularized feed-forward neural networks and hyper-reduced-order models obtained by combining the Proper Orthogonal Decomposition (POD) and Empirical Cubature Method (ECM) techniques. Training datasets are obtained from a Representative Volume Element (RVE) model of the composite microstructure with a number of randomly-distributed linear-elastic fibers surrounded by a matrix with pressure-dependent plasticity. The approaches are evaluated with a comprehensive set of numerical tests comprising pure stress cases and three different stress combinations relevant in the design of laminated composites. The models are assessed on their ability to accurately reproduce the training cases as well as on how well they are able to predict unseen stress combinations. Gains in execution time are compared by using the trained surrogates in the FE² model of an interlaminar shear test.

尽管是层合复合材料建模的一种流行方法，但中尺度本构模型经常难以表示任意载荷情况下的材料响应。就精度而言，更好的替代方法是使用FE ²技术在不损失一般性的情况下提升微观材料的性能，但相关的计算工作可能非常艰巨。因此，有趣的是探索替代代理建模策略，该策略可以保持FE ^2的尽可能高的保真度，同时仍具有计算效率。在这项工作中，在准确性，效率和校准工作方面比较了三种替代建模方法：Vogler等人的最新中观可塑性模型。（Vogler et al。，2013），通过结合适当的正交分解（POD）和经验性容器法（ECM）技术获得正则化前馈神经网络和超降阶模型。训练数据集是从复合微观结构的代表体积元（RVE）模型获得的，该模型具有许多随机分布的线性弹性纤维，并被具有压力依赖可塑性的基质包围。这些方法通过一套综合的数值测试进行评估，包括纯应力情况和与层压复合材料设计相关的三种不同应力组合。对模型进行准确评估的能力以及评估模型对未见压力组合的预测能力得到评估。²层间剪切试验模型。