AI 中文总结
本研究针对开孔材料多尺度模拟计算成本高的问题，提出混合专家（MoE）代理模型，可预测不同RVE的力学行为，无需额外训练即可适配新RVE，降低了模拟多个RVE的成本。
AI 中文摘要
对于开孔材料，将其微观结构特性纳入力学模拟对准确捕捉弹性变形构成重大挑战。为应对这一难题，多尺度方法是将所考虑材料的微观结构特性与宏观材料行为耦合的常用工具。然而，当追求高精度时，由于每个计算步骤中需要求解大量微观问题，这些多尺度计算的计算成本会非常高昂。在此，学习本构模型力学响应的代理模型可显著降低多尺度方法的计算成本。在作者团队此前的研究中，梁框架模型已被用于建模开孔材料的微观结构，该模型与基于神经网络的代理模型相结合，以近似给定代表性体积单元（RVE）的材料行为。本研究中，我们扩展了此前的研究，训练了一个更复杂的神经网络模型，以预测多个RVE的力学行为，这些RVE的最大孔径和孔径分布存在差异。具体而言，我们聚焦于混合专家（MoE）模型，比较不同的MoE架构及其在不同RVE上的性能。这种新方法降低了模拟多个RVE的计算成本，因为当考虑新的RVE时，MoE模型无需额外训练。
英文摘要
For open-porous materials, incorporating their microstructural properties into mechanical simulations poses a significant challenge for accurately capturing elastic deformation. To deal with this difficulty, multiscale methods are a common tool to couple characteristics of the microstructure of the considered material with the macroscopic material behavior. However, when desiring a high accuracy, these multiscale computations can be computationally very expensive due to the large number of microscopic problems which need to be solved in each compute step. Here, surrogate models that learn the mechanical response of the underlying constitutive model can significantly reduce the computational cost of multiscale approaches. In previous work by some of the authors, beam frame models have been used to model the microstructure of open-porous materials which have been combined with neural network-based surrogate models to approximate the material behavior of a given RVE (repesentative volume element). In this work, we extend our previous study by training a more complex neural network model to predict the mechanical behavior of several RVEs, differing in their maximum pore size and pore-size distribution. Concretely, we focus on mixture of expert (MoE) models and compare different MoE architectures as well as their performance across different RVEs. This novel approach reduces the computational cost of simulating multiple RVEs as the MoE model does not require additional training when new RVEs are considered.
