MBSNet: A deep learning model for multibody dynamics simulation and its application to a vehicle-track system
Published 2021 View Full Article
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Title
MBSNet: A deep learning model for multibody dynamics simulation and its application to a vehicle-track system
Authors
Keywords
MBSNet, Deep learning, Multibody dynamics simulation, Vehicle-track coupled dynamics simulation, Prediction, Challenge
Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 157, Issue -, Pages 107716
Publisher
Elsevier BV
Online
2021-02-24
DOI
10.1016/j.ymssp.2021.107716
References
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Related references
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- (2020) S. Bruni et al. MULTIBODY SYSTEM DYNAMICS
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- (2010) Stefano Falomi et al. WEAR
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- (2009) Jerry Evans et al. VEHICLE SYSTEM DYNAMICS
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