This contribution presents a meta-modeling framework that employs artificial intelligence to design a neural network that replicates the path-dependent constitutive responses of composite materials sampled by a numerical testing procedure of Representative Volume Elements (RVE). A Deep Reinforcement Learning (DRL) combinatorics game is invented to automatically search for the optimal set of hyper-parameters from a decision tree. Besides the typical hyper-parameters for ANN training, such as the network topology, the size and composition of the considered training data are incorporated as additional hyper-parameters to help investigate the amount of data necessary for training and validation. The proposed meta modeling framework is able to identify hyper-parameter configurations with a weighted trade-off between prediction accuracy and computational cost. The capabilities and limitations of the introduced framework are shown and discussed via several numerical examples. Moreover, the possibility of transferring the gained knowledge of hyper-parameters among different RVE is explored in numerical experiments.

该贡献提供了一个元模型框架，该框架利用人工智能设计了一个神经网络，该神经网络复制了通过代表体积元素（RVE）的数值测试程序采样的复合材料的路径依赖本构响应。发明了一种深度强化学习（DRL）组合游戏，可以从决策树中自动搜索最佳的超参数集。除了用于ANN训练的典型超参数（例如网络拓扑）之外，还考虑了训练数据的大小和组成作为其他超参数，以帮助调查训练和验证所需的数据量。所提出的元建模框架能够识别超参数配置，并在预测精度和计算成本之间进行权衡。通过几个数字示例显示并讨论了所引入框架的功能和局限性。此外，在数值实验中探索了在不同的RVE之间转移获得的超参数知识的可能性。