Presentation
Lyes Nechak is an Associate Professor at École Centrale de Lyon where he obtained his Habilitation to Supervise Research (HDR) in April 2024. In November 2011, he defended a PhD in Automation and Mechanics at the University of Haute-Alsace (UHA), focusing on robust approaches for analyzing and predicting the dynamic behavior of non-linear systems, with applications to systems subjected to friction-induced instabilities. He also holds a Master’s degree (Master 2) in Information Systems and Communication (ISC) obtained in 2008 from UHA, as well as Magister and Engineer degrees in Automation, earned in 2007 and 2003, respectively, from Mouloud Mammeri Université Mouloud Mammeri de Tizi-Ouzou (UMMTO). He continues to conduct research at the Tribology and System Dynamics Laboratory (LTDS), focusing on model order reduction and meta-modeling approaches for the prediction, analysis, observation, and control of complex dynamic systems. He is also, at École Centrale de Lyon, responsible for the Master’s program in Industrial Engineering, which offers two tracks: DIAGI (Data and Artificial Intelligence in Industrial Engineering) and MAGIF (Advanced Methods in the Industrial Engineering of the Future). He delivers multidisciplinary courses in the MSGMGC and EEA departments, in the Mechanical Engineering department at ENISE (Ecole Nationale d’Ingénieurs de Saint-Etienne), and at École Centrale de Pékin.
Research projects
His research activities focus on model reduction and metamodeling for the analysis, prediction, observation, and control of complex dynamical systems. Academic and industrial collaborations are being developed around projects including, among others:
- Reduction of linear and nonlinear parametric models, as well as metamodeling for the control of dynamical systems (collaborations withwith the IOGS/Paris-Saclay and the ISIR/Sorbonne Université)
- Identification of singularities in composite structures through metamodeling (academic collaboration within the framework of the China Scholarship Council)
- Observation and monitoring of the health state of industrial machines using machine learning techniques for decision-support purposes (industrial collaboration with ACOEM)
- Sensitivity analysis, optimization, and monitoring of aluminum structure assembly processes using machine learning techniques (collaboration with VALEO)
- Design and sensitivity analysis, using the FAST approach, of the vibro-acoustic performance of a railway damper (collaboration with ALSTOM)
Training activities
- Structural Mechanics,
- Mechanical Engineering,
- Continuous Systems Automation,
- Modeling and Optimization,
- Complex System Reliability,
- Diagnostics and Operational Safety