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2020 – today
- 2024
[i6]Jonathan Viquerat, Philippe Meliga, Pablo Jeken, Elie Hachem:
Beacon, a lightweight deep reinforcement learning benchmark library for flow control. CoRR abs/2402.17402 (2024)- [i5]Paul Garnier, Jonathan Viquerat, Elie Hachem:
Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics. CoRR abs/2409.11899 (2024) - 2023
- [j10]Jonathan Viquerat
, Régis Duvigneau, P. Meliga, Alexander Kuhnle, Elie Hachem:
Policy-based optimization: single-step policy gradient method seen as an evolution strategy. Neural Comput. Appl. 35(1): 449-467 (2023) - [i4]Jonathan Viquerat, Elie Hachem:
Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications. CoRR abs/2304.12330 (2023) - 2022
- [j9]Ying Zhao, Liang Li, Stéphane Lanteri, Jonathan Viquerat:
Dynamic metasurface control using Deep Reinforcement Learning. Math. Comput. Simul. 197: 377-395 (2022) - [j8]J. Chen, Jonathan Viquerat, F. Heymes, Elie Hachem:
A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles. Neural Comput. Appl. 34(8): 6289-6305 (2022) - 2021
- [j7]Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, Elie Hachem:
Direct shape optimization through deep reinforcement learning. J. Comput. Phys. 428: 110080 (2021) - [j6]Elie Hachem, Hassan Ghraieb, Jonathan Viquerat, Aurélien Larcher, P. Meliga:
Deep reinforcement learning for the control of conjugate heat transfer. J. Comput. Phys. 436: 110317 (2021) - 2020
- [i3]Hassan Ghraieb, Jonathan Viquerat, Aurélien Larcher, P. Meliga, Elie Hachem:
Optimization and passive flow control using single-step deep reinforcement learning. CoRR abs/2006.02979 (2020)
2010 – 2019
- 2019
- [i2]Paul Garnier, Jonathan Viquerat, Jean Rabault, Aurélien Larcher, Alexander Kuhnle, Elie Hachem:
A review on Deep Reinforcement Learning for Fluid Mechanics. CoRR abs/1908.04127 (2019) - [i1]Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Elie Hachem:
Direct shape optimization through deep reinforcement learning. CoRR abs/1908.09885 (2019) - 2018
- [j5]Nikolai Schmitt, Claire Scheid, Jonathan Viquerat, Stéphane Lanteri:
Simulation of three-dimensional nanoscale light interaction with spatially dispersive metals using a high order curvilinear DGTD method. J. Comput. Phys. 373: 210-229 (2018) - 2017
- [j4]Stéphane Lanteri, Claire Scheid, Jonathan Viquerat:
Analysis of a Generalized Dispersive Model Coupled to a DGTD Method with Application to Nanophotonics. SIAM J. Sci. Comput. 39(3) (2017) - 2016
- [j3]Nikolai Schmitt, Claire Scheid, Stéphane Lanteri, Antoine Moreau, Jonathan Viquerat:
A DGTD method for the numerical modeling of the interaction of light with nanometer scale metallic structures taking into account non-local dispersion effects. J. Comput. Phys. 316: 396-415 (2016) - 2015
- [j2]Jonathan Viquerat, Claire Scheid:
A 3D curvilinear discontinuous Galerkin time-domain solver for nanoscale light-matter interactions. J. Comput. Appl. Math. 289: 37-50 (2015) - 2014
- [j1]Raphaël Léger, Jonathan Viquerat, Clément Durochat, Claire Scheid, Stéphane Lanteri:
A parallel non-conforming multi-element DGTD method for the simulation of electromagnetic wave interaction with metallic nanoparticles. J. Comput. Appl. Math. 270: 330-342 (2014)
Coauthor Index
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