In this paper, we introduce a machine learning-based simulation framework of general-purpose multibody dynamics (MBD). The aim of the framework is to construct a well-trained meta-model of MBD systems, based on a deep neural network (DNN). Since the main advantage of the meta-model is the enhancement of computational efficiency in returning solutions, the modeling would be beneficial for solving highly complex MBD problems in a short time. Furthermore, for dynamics problems, not only the accuracy but also the smoothness in time of motion solutions, such as displacement, velocity, and acceleration, are essential aspects to consider. We analyze and discuss the influence of training data structures on both aspects of solutions. As a result of the introduced approach, the meta-model provides motion estimation of system dynamics without solving an analytical equation of motion or a numerical solver. Numerical tests demonstrate the performance of the proposed meta-modeling for representing several MBD systems.

在本文中，我们介绍了一种基于机器学习的通用多体动力学（MBD）仿真框架。该框架的目的是基于深度神经网络（DNN）构建MBD系统的训练有素的元模型。由于元模型的主要优点是在返回解决方案中提高了计算效率，因此该建模将有利于在短时间内解决高度复杂的MBD问题。此外，对于动力学问题，不仅要考虑运动解决方案的准确性，而且要考虑运动解决方案在时间上的平滑度，例如位移，速度和加速度。我们分析和讨论培训数据结构对解决方案的两个方面的影响。由于采用了这种方法，元模型提供了系统动力学的运动估计，而无需求解运动的解析方程或数值求解器。数值测试证明了所提出的用于代表几个MBD系统的元模型的性能。