Deep learning of multibody minimal coordinates
Author:
Affiliation:
1. LMSD Department of Mechanical Engineering KU Leuven
2. DMMS Lab Flanders Make
Publisher
Wiley
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/pamm.202000348
Reference6 articles.
1. Javier Garcia de Jalón and Eduardo Bayo. Kinematic and dynamic simulation of multibody systems: the real-time challenge. Springer Science & Business Media 2012.
2. Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. science 313(5786):504–507 2006.
3. Andrea Angeli Wim Desmet and Frank Naets. Deep learning for model order reduction of multibody systems to minimal coordinates. Computer Methods in Applied Mechanics and Engineering 373:113517 2021.
4. Dan Simon. Optimal state estimation: Kalman H infinity and nonlinear approaches. John Wiley & Sons 2006.
5. Andrea Angeli Frank Naets and Wim Desmet. A machine learning approach for minimal coordinate multibody simulation. In Multibody Dynamics pages 417–424. Springer 2019.
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