Gaël Poëtte

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 15

Top Topics

Monte Carlo Methods

Top Venues

J. Comput. Phys.

J. Sci. Comput.

Math. Comput. Simul.

Publications

Paul Novello, Gaël Poëtte, David Lugato, Simon Peluchon, Pietro Marco Congedo
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). J. Comput. Phys. 498 (2024)
Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault
An Abstract Lyapunov Control Optimizer: Local Stabilization and Global Convergence. CoRR (2024)
Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault
Deterministic Neural Networks Optimization from a Continuous and Energy Point of View. J. Sci. Comput. 96 (1) (2023)
Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo
Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. J. Sci. Comput. 94 (3) (2023)
Gaël Poëtte
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. 474 (2023)
Gaël Poëtte, Emeric Brun
computations with the Monte Carlo resolution of generalised Polynomial Chaos based reduced models. J. Comput. Phys. 456 (2022)
Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo
Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. CoRR (2022)
Gaël Poëtte
Efficient uncertainty propagation for photonics: Combining Implicit Semi-analog Monte Carlo (ISMC) and Monte Carlo generalised Polynomial Chaos (MC-gPC). J. Comput. Phys. 450 (2022)
Paul Novello, Gaël Poëtte, David Lugato, Simon Peluchon, Pietro Marco Congedo
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). CoRR (2022)
Paul Novello, Gaël Poëtte, David Lugato, Pietro Congedo
Variance Based Samples Weighting for Supervised Deep Learning. CoRR (2021)
Gaël Poëtte, Xavier Valentin
A new Implicit Monte-Carlo scheme for photonics (without teleportation error and without tilts). J. Comput. Phys. 412 (2020)
Gaël Poëtte
Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation. Math. Comput. Simul. 177 (2020)
Gaël Poëtte
A gPC-intrusive Monte-Carlo scheme for the resolution of the uncertain linear Boltzmann equation. J. Comput. Phys. 385 (2019)
Adrien Bernede, Gaël Poëtte
An Unsplit Monte-Carlo solver for the resolution of the linear Boltzmann equation coupled to (stiff) Bateman equations. J. Comput. Phys. 354 (2018)
Gaël Poëtte, Alexandre Birolleau, Didier Lucor
Iterative Polynomial Approximation Adapting to Arbitrary Probability Distribution. SIAM J. Numer. Anal. 53 (3) (2015)
Gaël Poëtte, Didier Lucor
Non intrusive iterative stochastic spectral representation with application to compressible gas dynamics. J. Comput. Phys. 231 (9) (2012)
Gaël Poëtte, Bruno Després, Didier Lucor
Uncertainty quantification for systems of conservation laws. J. Comput. Phys. 228 (7) (2009)