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

TL;DR

This paper presents a machine learning framework using deep neural networks to create data-driven meta-models for multibody dynamics, enabling accurate motion prediction without solving complex equations.

Contribution

It introduces a novel DNN-based approach for modeling multibody systems, bypassing traditional analytical solutions and improving simulation efficiency.

Findings

Meta-model accurately predicts system motion

Framework reduces computational complexity

Effective for various multibody systems

Abstract

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.