A parallel non-conforming multi-element DGTD method for the simulation of electromagnetic wave interaction with metallic nanoparticles. - Raphaël Léger; Jonathan Viquerat; Clément Durochat; Claire Scheid; Stéphane Lanteri (J. Comput. Appl. Math., 2014)

A 3D curvilinear discontinuous Galerkin time-domain solver for nanoscale light-matter interactions. - Jonathan Viquerat; Claire Scheid (J. Comput. Appl. Math., 2015)

A DGTD method for the numerical modeling of the interaction of light with nanometer scale metallic structures taking into account non-local dispersion effects. - Nikolai Schmitt; Claire Scheid; Stéphane Lanteri; Antoine Moreau; Jonathan Viquerat (J. Comput. Phys., 2016)

Analysis of a Generalized Dispersive Model Coupled to a DGTD Method with Application to Nanophotonics. - Stéphane Lanteri; Claire Scheid; Jonathan Viquerat (SIAM J. Sci. Comput., 2017)

Simulation of three-dimensional nanoscale light interaction with spatially dispersive metals using a high order curvilinear DGTD method. - Nikolai Schmitt; Claire Scheid; Jonathan Viquerat; Stéphane Lanteri (J. Comput. Phys., 2018)

A review on Deep Reinforcement Learning for Fluid Mechanics. - Paul Garnier; Jonathan Viquerat; Jean Rabault; Aurélien Larcher; Alexander Kuhnle; Elie Hachem (CoRR, 2019)

Direct shape optimization through deep reinforcement learning. - Jonathan Viquerat; Jean Rabault; Alexander Kuhnle; Hassan Ghraieb; Elie Hachem (CoRR, 2019)

Optimization and passive flow control using single-step deep reinforcement learning. - Hassan Ghraieb; Jonathan Viquerat; Aurélien Larcher; P. Meliga; Elie Hachem (CoRR, 2020)

Deep reinforcement learning for the control of conjugate heat transfer. - Elie Hachem; Hassan Ghraieb; Jonathan Viquerat; Aurélien Larcher; P. Meliga (J. Comput. Phys., 2021)

Direct shape optimization through deep reinforcement learning. - Jonathan Viquerat; Jean Rabault; Alexander Kuhnle; Hassan Ghraieb; Aurélien Larcher; Elie Hachem (J. Comput. Phys., 2021)

Dynamic metasurface control using Deep Reinforcement Learning. - Ying Zhao; Liang Li 001; Stéphane Lanteri; Jonathan Viquerat (Math. Comput. Simul., 2022)

A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles. - J. Chen; Jonathan Viquerat; F. Heymes; Elie Hachem (Neural Comput. Appl., 2022)

Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications. - Jonathan Viquerat; Elie Hachem (CoRR, 2023)

Policy-based optimization: single-step policy gradient method seen as an evolution strategy. - Jonathan Viquerat; Régis Duvigneau; P. Meliga; Alexander Kuhnle; Elie Hachem (Neural Comput. Appl., 2023)

Beacon, a lightweight deep reinforcement learning benchmark library for flow control. - Jonathan Viquerat; Philippe Meliga; Pablo Jeken; Elie Hachem (CoRR, 2024)

Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics. - Paul Garnier; Jonathan Viquerat; Elie Hachem (CoRR, 2024)

MeshMask: Physics-Based Simulations with Masked Graph Neural Networks. - Paul Garnier; Vincent Lannelongue; Jonathan Viquerat; Elie Hachem (ICLR, 2025)

Dragonfly: a modular deep reinforcement learning library. - Jonathan Viquerat; Paul Garnier; Amir-Hossein Bateni; Elie Hachem (CoRR, 2025)