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Researcher
Wannes De Groote
- Disciplines:Multimedia processing, Modelling
Affiliations
- Department of Electromechanical, Systems and Metal Engineering (Department)
Member
From1 Aug 2017 → Today
Projects
1 - 1 of 1
- Dynamic hybrid models for virtual scenario testing and optimal design of drivetrainsFrom1 Jan 2019 → 9 Oct 2022Funding: FWO Strategic Basic Research Grant
Publications
1 - 7 of 7
- Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch(2020)
Authors: Wannes De Groote, Tom Lefebvre, Georges Tod, Nele De Geeter, Bruno Depraetere, Suzanne Van Poppel, Guillaume Crevecoeur
- Hybrid derivative functions for identification of unknown loads and physical parameters with application on slider-crank mechanism(2019)
Authors: Wannes De Groote, Edward Kikken, Srajan Goyal, Sofie Van Hoecke, Erik Hostens, Guillaume Crevecoeur
Pages: 1049 - 1054 - Parametric uncertainty quantification using polynomial chaos expansions applied to a wet friction clutch model(2019)
Authors: Georges Tod, Wannes De Groote, Tom Lefebvre, Nele De Geeter, Bruno Depraetere, Guillaume Crevecoeur
Pages: 185 - 191 - Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks
Authors: Wannes De Groote, Sofie Van Hoecke, Guillaume Crevecoeur
- Neural network augmented physics models for systems with partially unknown dynamics : application to slider-crank mechanism
Authors: Wannes De Groote, Edward Kikken, Erik Hostens, Sofie Van Hoecke, Guillaume Crevecoeur
Pages: 103 - 114 - Physics-based neural network models for prediction of cam-follower dynamics beyond nominal operations
Authors: Wannes De Groote, Sofie Van Hoecke, Guillaume Crevecoeur
Pages: 2345 - 2355 - Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications
Authors: Wannes De Groote