Uncertainty quantification for systems of conservation laws. - Gaël Poëtte; Bruno Després; Didier Lucor (J. Comput. Phys., 2009)

Non intrusive iterative stochastic spectral representation with application to compressible gas dynamics. - Gaël Poëtte; Didier Lucor (J. Comput. Phys., 2012)

Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method. - Bruno Després; Gaël Poëtte; Didier Lucor (Uncertainty Quantification in Computational Fluid Dynamics, 2013)

Iterative Polynomial Approximation Adapting to Arbitrary Probability Distribution. - Gaël Poëtte; Alexandre Birolleau; Didier Lucor (SIAM J. Numer. Anal., 2015)

An Unsplit Monte-Carlo solver for the resolution of the linear Boltzmann equation coupled to (stiff) Bateman equations. - Adrien Bernede; Gaël Poëtte (J. Comput. Phys., 2018)

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 (2019)

A gPC-intrusive Monte-Carlo scheme for the resolution of the uncertain linear Boltzmann equation. - Gaël Poëtte (J. Comput. Phys., 2019)

A new Implicit Monte-Carlo scheme for photonics (without teleportation error and without tilts). - Gaël Poëtte; Xavier Valentin (J. Comput. Phys., 2020)

Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation. - Gaël Poëtte (Math. Comput. Simul., 2020)

Variance Based Samples Weighting for Supervised Deep Learning. - Paul Novello; Gaël Poëtte; David Lugato; Pietro Marco Congedo (CoRR, 2021)

Efficient uncertainty propagation for photonics: Combining Implicit Semi-analog Monte Carlo (ISMC) and Monte Carlo generalised Polynomial Chaos (MC-gPC). - Gaël Poëtte (J. Comput. Phys., 2022)

Efficient uncertain keff computations with the Monte Carlo resolution of generalised Polynomial Chaos based reduced models. - Gaël Poëtte; Emeric Brun (J. Comput. Phys., 2022)

Multigroup-like MC resolution of generalised Polynomial Chaos reduced models of the uncertain linear Boltzmann equation (+discussion on hybrid intrusive/non-intrusive uncertainty propagation). - Gaël Poëtte (J. Comput. Phys., 2023)

Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. - Paul Novello; Gaël Poëtte; David Lugato; Pietro Marco Congedo (J. Sci. Comput., 2023)

Deterministic Neural Networks Optimization from a Continuous and Energy Point of View. - Bilel Bensaid; Gaël Poëtte; Rodolphe Turpault (J. Sci. Comput., 2023)

An Abstract Lyapunov Control Optimizer: Local Stabilization and Global Convergence. - Bilel Bensaid; Gaël Poëtte; Rodolphe Turpault (CoRR, 2024)

Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). - Paul Novello; Gaël Poëtte; David Lugato; Simon Peluchon; Pietro Marco Congedo (J. Comput. Phys., 2024)