A hybrid twin based on machine learning enhanced reduced order model for real-time simulation of magnetic bearings

C Ghnatios, S Rodriguez, J Tomezyk, Y Dupuis… - Advanced Modeling and …, 2024 - Springer
The simulation of magnetic bearings involves highly non-linear physics, with high
dependency on the input variation. Moreover, such a simulation is time consuming and can't …

Optimal trajectory planning combining model-based and data-driven hybrid approaches

C Ghnatios, D Di Lorenzo, V Champaney… - Advanced Modeling and …, 2024 - Springer
Trajectory planning aims at computing an optimal trajectory through the minimization of a
cost function. This paper considers four different scenarios:(i) the first concerns a given …

A Parsimonious Separated Representation Empowering PINN–PGD-Based Solutions for Parametrized Partial Differential Equations

C Ghnatios, F CHINESTA SORIA - 2024 - sam.ensam.eu
The efficient solution (fast and accurate) of parametric partial differential equations (pPDE) is
of major interest in many domains of science and engineering, enabling evaluations of the …

First Steps in Probabilistic Hybrid Twin Extrapolation Based on Nonparametric Probabilistic Method and Machine Learning Algorithms

C Ghnatios - 2023 Fifth International Conference on Advances …, 2023 - ieeexplore.ieee.org
With emerging mechanical engineering applications on a daily basis, the incorporation of
data into models and data-driven modeling are subjected to constant improvement …

Advanced model order reduction and data-driven technologies enabling physics-augmented digital twins

V Champaney - 2023 - pastel.hal.science
In the 20th century, engineering made remarkable strides in various fields, while other
disciplines turned to data for diagnostic and prognostic purposes. Recognizing the potential …