Data-driven simulation for general-purpose multibody dynamics using Deep Neural Networks

Hee Sun Choi, Junmo An, Seongji Han, Jin Gyun Kim, Jae Yoon Jung, Juhwan Choi, Grzegorz Orzechowski, Aki Mikkola, Jin Hwan Choi

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)419-454
Number of pages36
JournalMultibody System Dynamics
Issue number4
Publication statusPublished - Apr 2021


  • Data-driven simulation
  • Deep neural network
  • Feed forward network
  • Meta-model
  • Multibody dynamics


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