Gaël Poëtte - RankMe

19 papers Journal 17

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). J. Comput. Phys. Paul Novello, Gaël Poëtte, David Lugato, Simon Peluchon, Pietro Marco Congedo
2024	J	`jnl`	An Abstract Lyapunov Control Optimizer: Local Stabilization and Global Convergence. CoRR Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault
2023	J	`jnl`	Deterministic Neural Networks Optimization from a Continuous and Energy Point of View. J. Sci. Comput. Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault
2023	J	`jnl`	Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. J. Sci. Comput. Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo
2023	J	`jnl`	Multigroup-like MC resolution of generalised Polynomial Chaos reduced models of the uncertain linear Boltzmann equation (+discussion on hybrid intrusive/non-intrusive uncertainty propagation). J. Comput. Phys. Gaël Poëtte
2022	J	`jnl`	Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). CoRR Paul Novello, Gaël Poëtte, David Lugato, Simon Peluchon, Pietro Marco Congedo
2022	J	`jnl`	Efficient uncertain J. Comput. Phys. Gaël Poëtte, Emeric Brun
2022	J	`jnl`	Efficient uncertainty propagation for photonics: Combining Implicit Semi-analog Monte Carlo (ISMC) and Monte Carlo generalised Polynomial Chaos (MC-gPC). J. Comput. Phys. Gaël Poëtte
2022	J	`jnl`	Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. CoRR Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo
2021	J	`jnl`	Variance Based Samples Weighting for Supervised Deep Learning. CoRR Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo
2020	J	`jnl`	A new Implicit Monte-Carlo scheme for photonics (without teleportation error and without tilts). J. Comput. Phys. Gaël Poëtte, Xavier Valentin
2020	J	`jnl`	Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation. Math. Comput. Simul. Gaël Poëtte
2019	J	`jnl`	A gPC-intrusive Monte-Carlo scheme for the resolution of the uncertain linear Boltzmann equation. J. Comput. Phys. Gaël Poëtte
2019	—	`book`	Contribution to the Mathematical and numerical Analysis of uncertain Systems of conservation Laws and of the linear and nonlinear Boltzmann equation. (Contribution a l'analyse Mathematique et numerique des systemes de lois de conservation incertains et de l'equation de Boltzmann Lineaire et non-Lineaire). Gaël Poëtte
2018	J	`jnl`	An Unsplit Monte-Carlo solver for the resolution of the linear Boltzmann equation coupled to (stiff) Bateman equations. J. Comput. Phys. Adrien Bernede, Gaël Poëtte
2015	J	`jnl`	Iterative Polynomial Approximation Adapting to Arbitrary Probability Distribution. SIAM J. Numer. Anal. Gaël Poëtte, Alexandre Birolleau, Didier Lucor
2013	—	`ch.`	Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method. Uncertainty Quantification in Computational Fluid Dynamics Bruno Després, Gaël Poëtte, Didier Lucor
2012	J	`jnl`	Non intrusive iterative stochastic spectral representation with application to compressible gas dynamics. J. Comput. Phys. Gaël Poëtte, Didier Lucor
2009	J	`jnl`	Uncertainty quantification for systems of conservation laws. J. Comput. Phys. Gaël Poëtte, Bruno Després, Didier Lucor