基于投影的有限塑性模型降阶的神经网络加速：在RVE中的应用
Neural-network acceleration of projection-based model-order-reduction for finite plasticity: Application to RVEs
摘要：
与传统的基于投影的模型降阶相比，其神经网络加速的优点是在线模拟是无方程的，这意味着不需要迭代求解方程组。因此，不需要构造刚度矩阵，并且每个增量仅需要计算一次应力更新。在这方面的贡献，一个经常性的神经网络的开发，以加速基于投影的模型降阶的弹塑性力学行为的RVE。与仅模拟宏观变形（路径）和宏观应力之间的关系的神经网络相比，基于投影的模型降阶的神经网络加速以每次增量计算一次该信息为代价保留了所有微观结构信息。
英文摘要：
Compared to conventional projection-based model-order-reduction, its neural-network acceleration has the advantage that the online simulations are equation-free, meaning that no system of equations needs to be solved iteratively. Consequently, no stiffness matrix needs to be constructed and the stress update needs to be computed only once per increment. In this contribution, a recurrent neural network is developed to accelerate a projection-based model-order-reduction of the elastoplastic mechanical behaviour of an RVE. In contrast to a neural network that merely emulates the relation between the macroscopic deformation (path) and the macroscopic stress, the neural network acceleration of projection-based model-order-reduction preserves all microstructural information, at the price of computing this information once per increment.
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