A study of mechanism-data hybrid-driven method for multibody system via physics-informed neural network

Numerical simulation plays an important role in the dynamic analysis of multibody system. With the rapid development of computer science, the numerical solution technology has been further developed. Recently, data-driven method has become a very popular computing method. However, due to lack of necessary mechanism information of the traditional pure data-driven methods based on neural network, its numerical accuracy cannot be guaranteed for strong nonlinear system. Therefore, this work proposes a mechanism-data hybrid-driven strategy for solving nonlinear multibody system based on physics-informed neural network to overcome the limitation of traditional data-driven methods. The strategy proposed in this paper introduces scaling coefficients to introduce the dynamic model of multibody system into neural network, ensuring that the training results of neural network conform to the mechanics principle of the system, thereby ensuring the good reliability of the data-driven method. Finally, the stability, generalization ability and numerical accuracy of the proposed method are discussed and analyzed using three typical multibody systems, and the constrained default situations can be controlled within the range of 10(-2)-10(-4).