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Onderzoeker
Wannes De Groote
- Disciplines:Multimediaverwerking, Modellering
Affiliaties
- Vakgroep Elektromechanica, Systeem- en Metaalengineering (Departement)
Lid
Vanaf1 aug 2017 → Heden
Projecten
1 - 1 of 1
- Dynamische hybride modellering voor virtuele test scenario’s en optimaal design van aandrijflijnenVanaf1 jan 2019 → 9 okt 2022Financiering: FWO Strategische Onderzoeksbeurs
Publicaties
1 - 7 van 7
- Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch(2020)
Auteurs: 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)
Auteurs: Wannes De Groote, Edward Kikken, Srajan Goyal, Sofie Van Hoecke, Erik Hostens, Guillaume Crevecoeur
Pagina's: 1049 - 1054 - Parametric uncertainty quantification using polynomial chaos expansions applied to a wet friction clutch model(2019)
Auteurs: Georges Tod, Wannes De Groote, Tom Lefebvre, Nele De Geeter, Bruno Depraetere, Guillaume Crevecoeur
Pagina's: 185 - 191 - Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks
Auteurs: Wannes De Groote, Sofie Van Hoecke, Guillaume Crevecoeur
- Neural network augmented physics models for systems with partially unknown dynamics : application to slider-crank mechanism
Auteurs: Wannes De Groote, Edward Kikken, Erik Hostens, Sofie Van Hoecke, Guillaume Crevecoeur
Pagina's: 103 - 114 - Physics-based neural network models for prediction of cam-follower dynamics beyond nominal operations
Auteurs: Wannes De Groote, Sofie Van Hoecke, Guillaume Crevecoeur
Pagina's: 2345 - 2355 - Hybrid physics-based neural network models for predicting nonlinear dynamics in mechatronic applications
Auteurs: Wannes De Groote