Abstract To achieve real-time simulations for flexible multibody dynamics (FMBD) systems, we suggest data-driven modeling based on deep neural networks (DNNs). While a DNN can represent system dynamics accurately, two main factors of FMBD systems require demanding computational costs for training a DNN. One is a fine discretization of flexible bodies, which produces a large number of training data. The other is the nonlinearity of FMBD, which requires train DNN models to have numerous weight and bias parameters. To overcome these difficulties, we propose a data-driven modeling algorithm for training a DNN efficiently that consists of two steps. First, sets of randomly chosen coarse data sequentially train a DNN model. This helps speed up the training process, even for highly parametrized DNNs. At some point, the model no longer improves, and introducing an error correction step increases the performance of the model. The proposed algorithm is easy to employ and utilizes an efficient size of training data while achieving high performance of the DNN as demonstrated by numerical examples.

中文翻译：

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基于 DNN 的数据驱动建模，采用粗样本数据进行实时灵活的多体动力学仿真

摘要 为了实现灵活多体动力学 (FMBD) 系统的实时仿真，我们建议基于深度神经网络 (DNN) 的数据驱动建模。虽然 DNN 可以准确地表示系统动力学，但 FMBD 系统的两个主要因素需要高要求的计算成本来训练 DNN。一种是柔性体的精细离散化，产生大量的训练数据。另一个是FMBD的非线性，这需要训练的DNN模型具有众多的权重和偏置参数。为了克服这些困难，我们提出了一种数据驱动的建模算法，用于有效地训练 DNN，它由两个步骤组成。首先，随机选择的粗数据集顺序训练 DNN 模型。这有助于加快训练过程，即使对于高度参数化的 DNN。在某些时候，模型不再改进，引入纠错步骤可以提高模型的性能。所提出的算法易于使用并利用有效大小的训练数据，同时实现 DNN 的高性能，如数值示例所示。