# Data-driven simulation for general purpose multibody dynamics using deep neural networks

arXiv: https://arxiv.org/abs/1909.02391 · HTML: https://mlsanity.com/paper/1909.02391/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems](https://mlsanity.com/paper/2501.07373/) (2025)
- [SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems](https://mlsanity.com/paper/2409.18272/) (2024)
- [Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems](https://mlsanity.com/paper/2504.13768/) (2025)
- [Solving Coupled Differential Equation Groups Using PINO-CDE](https://mlsanity.com/paper/2210.00222/) (2022)
- [Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach](https://mlsanity.com/paper/2407.17156/) (2024)
- [Real-Time Structural Deflection Estimation in Hydraulically Actuated Systems Using 3D Flexible Multibody Simulation and DNNs](https://mlsanity.com/paper/2503.07528/) (2025)
- [Latent-Lagrangian Neural Networks for Reduced Order Modeling of Non-autonomous Nonlinear Dynamical Systems](https://mlsanity.com/paper/2610.04723/) (2026)

## Abstract (arXiv)

In this paper, a machine learning-based simulation framework of general-purpose multibody dynamics is introduced. The aim of the framework is to generate a well-trained meta-model of multibody dynamics (MBD) systems. To this end, deep neural network (DNN) is employed to the framework so as to construct data-based meta-model representing multibody systems. Constructing well-defined training data set with time variable is essential to get accurate and reliable motion data such as displacement, velocity, acceleration, and forces. As a result of the introduced approach, the meta-model provides motion estimation of system dynamics without solving the analytical equations of motion. The performance of the proposed DNN meta-modeling was evaluated to represent several MBD systems.