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ORCIDGaël Poëtte
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2020 – today
- 2024
[j13]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: 112700 (2024)- [i4]Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault:
An Abstract Lyapunov Control Optimizer: Local Stabilization and Global Convergence. CoRR abs/2407.01019 (2024) - 2023
- [j12]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: 111825 (2023) - [j11]Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo:
Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. J. Sci. Comput. 94(3): 45 (2023) - [j10]Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault:
Deterministic Neural Networks Optimization from a Continuous and Energy Point of View. J. Sci. Comput. 96(1): 14 (2023) - 2022
- [j9]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: 110807 (2022) - [j8]Gaël Poëtte, Emeric Brun:
Efficient uncertain keff computations with the Monte Carlo resolution of generalised Polynomial Chaos based reduced models. J. Comput. Phys. 456: 111007 (2022) - [i3]Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo:
Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. CoRR abs/2207.06216 (2022) - [i2]Paul Novello, Gaël Poëtte, David Lugato, Simon Peluchon, Pietro Marco Congedo:
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). CoRR abs/2209.13434 (2022) - 2021
- [i1]Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo:
Variance Based Samples Weighting for Supervised Deep Learning. CoRR abs/2101.07561 (2021) - 2020
- [j7]Gaël Poëtte, Xavier Valentin:
A new Implicit Monte-Carlo scheme for photonics (without teleportation error and without tilts). J. Comput. Phys. 412: 109405 (2020) - [j6]Gaël Poëtte:
Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation. Math. Comput. Simul. 177: 24-45 (2020)
2010 – 2019
- 2019
- [b1]Gaël Poëtte:
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). University of Bordeaux, France, 2019 - [j5]Gaël Poëtte:
A gPC-intrusive Monte-Carlo scheme for the resolution of the uncertain linear Boltzmann equation. J. Comput. Phys. 385: 135-162 (2019) - 2018
- [j4]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: 211-241 (2018) - 2015
- [j3]Gaël Poëtte, Alexandre Birolleau, Didier Lucor:
Iterative Polynomial Approximation Adapting to Arbitrary Probability Distribution. SIAM J. Numer. Anal. 53(3): 1559-1584 (2015) - 2013
- [p1]Bruno Després, Gaël Poëtte, Didier Lucor:
Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method. Uncertainty Quantification in Computational Fluid Dynamics 2013: 105-149 - 2012
- [j2]Gaël Poëtte, Didier Lucor:
Non intrusive iterative stochastic spectral representation with application to compressible gas dynamics. J. Comput. Phys. 231(9): 3587-3609 (2012)
2000 – 2009
- 2009
- [j1]Gaël Poëtte, Bruno Després, Didier Lucor:
Uncertainty quantification for systems of conservation laws. J. Comput. Phys. 228(7): 2443-2467 (2009)
Coauthor Index
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