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Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications

(2021)
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MLA
De Groote, Wannes. Hybrid Physics-Based Neural Network Models for Predicting Nonlinear Dynamics in Mechatronic Applications. Ghent University. Faculty of Engineering and Architecture, 2021.
APA
De Groote, W. (2021). Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications. Ghent University. Faculty of Engineering and Architecture, Ghent, Belgium.
Chicago author-date
De Groote, Wannes. 2021. “Hybrid Physics-Based Neural Network Models for Predicting Nonlinear Dynamics in Mechatronic Applications.” Ghent, Belgium: Ghent University. Faculty of Engineering and Architecture.
Chicago author-date (all authors)
De Groote, Wannes. 2021. “Hybrid Physics-Based Neural Network Models for Predicting Nonlinear Dynamics in Mechatronic Applications.” Ghent, Belgium: Ghent University. Faculty of Engineering and Architecture.
Vancouver
1.
De Groote W. Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications. [Ghent, Belgium]: Ghent University. Faculty of Engineering and Architecture; 2021.
IEEE
[1]
W. De Groote, “Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications,” Ghent University. Faculty of Engineering and Architecture, Ghent, Belgium, 2021.
@phdthesis{8726120,
  author       = {{De Groote, Wannes}},
  isbn         = {{9789463555401}},
  language     = {{eng}},
  pages        = {{XX, 196}},
  publisher    = {{Ghent University. Faculty of Engineering and Architecture}},
  school       = {{Ghent University}},
  title        = {{Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications}},
  year         = {{2021}},
}