activity
20212026
most citedModalPINN: an extension of Physics-Informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors

34 citations · 37 across the 5 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2026

On the wake and flapping dynamics of different aspect ratio flags

Gaétan Raynaud, Karen Mulleners

The flapping of flags is a classical problem involving fast and large amplitude deformations of a thin flexible plate and unsteady flow phenomena. We perform systematic time and sp…

physics.flu-dyn2024★ 2 cited

Event-based reconstruction of time-resolved centreline deformation of flapping flags

Gaetan Raynaud, Karen Mulleners

High-speed imaging is central to the experimental investigation of fast phenomena, like flapping flags. Event-based cameras use new types of sensors that address typical challenges…

cs.RO2024★ 1 cited

Highly agile flat swimming robot

Florian Hartmann, Mrudhula Baskaran, Gaetan Raynaud +3

Exploring bodies of water on their surface allows robots to efficiently communicate and harvest energy from the sun. On the water surface, however, robots often face highly unstruc…

cs.RO2022

Proprioceptive Sensing of Soft Tentacles with Model Based Reconstruction for Controller Optimization

Andrea Vicari, Nana Obayashi, Francesco Stella +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…

physics.flu-dyn2021★ 34 cited

ModalPINN: an extension of Physics-Informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors

Gaetan Raynaud, Sebastien Houde, Frederick P. Gosselin

Continuous reconstructions of periodic phenomena provide powerful tools to understand, predict and model natural situations and engineering problems. In line with the recent method…