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4
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Citations since 2017
Introduction
Fluid-structure interaction, Physics Informed Neural Networks (PINN), inverse problems and modal approach.
Previously at Laboratory for Multiscale Mechanics (LM2) in Montréal
Now working on data driven analysis and control in vortex dominated flows at Unsteady flow diagnostics laboratory (UNFoLD) in Lausanne
Publications
Publications (4)
The success of soft robots in displaying emergent behaviors is tightly linked to the compliant interaction with the environment. However, to exploit such phenomena, proprioceptive sensing methods which do not hinder their softness are needed. In this work we propose a new sensing approach for soft underwater slender structures based on embedded pre...
Continuous reconstructions of periodic phenomena provide powerful tools to understand, predict and model natural situations and engineering problems. In line with the recent method called Physics-Informed Neural Networks (PINN) where a multi layer perceptron directly approximates any physical quantity as a symbolic function of time and space coordi...
Continuous reconstructions of periodic phenomena provide powerful tools to understand, predict and model natural situations and engineering problems. In line with the recent method called Physics-Informed Neural Networks (PINN) where a multi layer perceptron directly approximates any physical quantity as a symbolic function of time and space coordi...
To address most scientific problems, two approaches usually stand : mathematical modelling or experimentation. Physics Informed Neural Network (PINN) is a recent numerical method that closes this gap using multi-layer perceptrons that approximate physical quantities. This allows resolution of ill-posed problems with a light formalism by optimizing...
