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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
1932013
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
1672021
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
1422021
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
692016
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
662020
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), 2019
56*2019
A review on deep reinforcement learning for fluid mechanics: An update
J Viquerat, P Meliga, A Larcher, E Hachem
Physics of Fluids 34 (11), 2022
472022
Graph neural networks for laminar flow prediction around random two-dimensional shapes
J Chen, E Hachem, J Viquerat
Physics of Fluids 33 (12), 123607, 2021
462021
U-net architectures for fast prediction of incompressible laminar flows
J Chen, J Viquerat, E Hachem
arXiv preprint arXiv:1910.13532, 2019
44*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
43*2021
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
43*2017
Simulation of electromagnetic waves propagation in nano-optics with a high-order discontinuous Galerkin time-domain method
J Viquerat
Université Nice Sophia Antipolis, 2015
36*2015
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
342013
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
Journal of Computational Physics 436, 110317, 2021
312021
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
292014
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
232018
Robust deep learning for emulating turbulent viscosities
A Patil, J Viquerat, A Larcher, G El Haber, E Hachem
Physics of Fluids 33 (10), 2021
15*2021
Policy-based optimization: Single-step policy gradient method seen as an evolution strategy
J Viquerat, R Duvigneau, P Meliga, A Kuhnle, E Hachem
Neural Computing and Applications 35 (1), 449-467, 2023
142023
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
122015
Single-step deep reinforcement learning for two-and three-dimensional optimal shape design
H Ghraieb, J Viquerat, A Larcher, P Meliga, E Hachem
AIP Advances 12 (8), 2022
112022
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Articles 1–20