Jonathan Viquerat
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Numerical analysis of the radial force produced by the Medtronic-CoreValve and Edwards-SAPIEN after transcatheter aortic valve implantation (TAVI)
S Tzamtzis, J Viquerat, J Yap, MJ Mullen, G Burriesci
Medical engineering & physics 35 (1), 125-130, 2013
A DGTD method for the numerical modeling of the interaction of light with nanometer scale metallic structures taking into account non-local dispersion effects
N Schmitt, C Scheid, S Lanteri, A Moreau, J Viquerat
Journal of Computational Physics 316, 396-415, 2016
Analysis of a generalized dispersive model coupled to a DGTD method with application to nanophotonics
S Lanteri, C Scheid, J Viquerat
SIAM Journal on Scientific Computing 39 (3), A831-A859, 2017
A review on deep reinforcement learning for fluid mechanics
P Garnier, J Viquerat, J Rabault, A Larcher, A Kuhnle, E Hachem
Computers & Fluids 225, 104973, 2021
Recent advances on a DGTD method for time-domain electromagnetics
S Descombes, C Durochat, S Lanteri, L Moya, C Scheid, J Viquerat
Photonics and Nanostructures-Fundamentals and Applications 11 (4), 291-302, 2013
Simulation of electromagnetic waves propagation in nano-optics with a high-order discontinuous Galerkin time-domain method
J Viquerat
Université Nice Sophia Antipolis, 2015
A parallel non-conforming multi-element DGTD method for the simulation of electromagnetic wave interaction with metallic nanoparticles
R Léger, J Viquerat, C Durochat, C Scheid, S Lanteri
Journal of computational and applied mathematics 270, 330-342, 2014
Direct shape optimization through deep reinforcement learning
J Viquerat, J Rabault, A Kuhnle, H Ghraieb, A Larcher, E Hachem
Journal of Computational Physics 428, 110080, 2021
A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number
J Viquerat, E Hachem
Computers & Fluids 210, 104645, 2020
Exploiting locality and translational invariance to design effective deep reinforcement learning control of the 1-dimensional unstable falling liquid film
V Belus, J Rabault, J Viquerat, Z Che, E Hachem, U Reglade
AIP Advances 9 (12), 125014, 2019
Simulation of three-dimensional nanoscale light interaction with spatially dispersive metals using a high order curvilinear DGTD method
N Schmitt, C Scheid, J Viquerat, S Lanteri
Journal of Computational Physics 373, 210-229, 2018
A 3D curvilinear discontinuous Galerkin time-domain solver for nanoscale light–matter interactions
J Viquerat, C Scheid
Journal of computational and applied mathematics 289, 37-50, 2015
U-net architectures for fast prediction of incompressible laminar flows
J Chen, J Viquerat, E Hachem
arXiv preprint arXiv:1910.13532, 2019
Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows
H Ghraieb, J Viquerat, A Larcher, P Meliga, E Hachem
Physical Review Fluids 6 (5), 053902, 2021
Simulation of near-field plasmonic interactions with a local approximation order discontinuous Galerkin time-domain method
J Viquerat, S Lanteri
Photonics and Nanostructures-Fundamentals and Applications 18, 43-58, 2016
Deep reinforcement learning for the control of conjugate heat transfer with application to workpiece cooling
E Hachem, H Ghraieb, J Viquerat, A Larcher, P Meliga
arXiv preprint arXiv:2011.15035, 2020
A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles
J Chen, J Viquerat, F Heymes, E Hachem
arXiv preprint arXiv:2104.03619, 2021
Efficient time‐domain numerical analysis of waveguides with tailored wideband pulses
J Viquerat
Microwave and Optical Technology Letters 61 (6), 1534-1539, 2018
Fitting experimental dispersion data with a simulated annealing method for nano-optics applications
J Viquerat
Journal of Nanophotonics 12 (3), 036014, 2018
A review on deep reinforcement learning for fluid mechanics: an update
J Viquerat, P Meliga, E Hachem
arXiv preprint arXiv:2107.12206, 2021
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