Analysis and Optimization of Dense gas flows: Application to organic Rankine cycles turbines. - Pietro Marco Congedo (2007)

A simplex-based numerical framework for simple and efficient robust design optimization. - Pietro Marco Congedo; Jeroen A. S. Witteveen; Gianluca Iaccarino (Comput. Optim. Appl., 2013)

A semi-intrusive deterministic approach to uncertainty quantification in non-linear fluid flow problems. - Rémi Abgrall; Pietro Marco Congedo (J. Comput. Phys., 2013)

A one-time truncate and encode multiresolution stochastic framework. - Rémi Abgrall; Pietro Marco Congedo; Gianluca Geraci (J. Comput. Phys., 2014)

Stochastic Discrete Equation Method (sDEM) for two-phase flows. - Rémi Abgrall; Pietro Marco Congedo; Gianluca Geraci; Maria Giovanna Rodio (J. Comput. Phys., 2015)

An adaptive strategy on the error of the objective functions for uncertainty-based derivative-free optimization. - Francesca Fusi; Pietro Marco Congedo (J. Comput. Phys., 2016)

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation. - Kunkun Tang; Pietro Marco Congedo; Rémi Abgrall (J. Comput. Phys., 2016)

A Novel Weakly-Intrusive Non-linear Multiresolution Framework for Uncertainty Quantification in Hyperbolic Partial Differential Equations. - Gianluca Geraci; Pietro Marco Congedo; Rémi Abgrall; Gianluca Iaccarino (J. Sci. Comput., 2016)

Towards a unified multiresolution scheme for treating discontinuities in differential equations with uncertainties. - Rémi Abgrall; Pietro Marco Congedo; Gianluca Geraci (Math. Comput. Simul., 2017)

Novel algorithm using Active Metamodel Learning and Importance Sampling: Application to multiple failure regions of low probability. - Nassim Razaaly; Pietro Marco Congedo (J. Comput. Phys., 2018)

Kriging-sparse Polynomial Dimensional Decomposition surrogate model with adaptive refinement. - Andrea F. Cortesi; Ghina Jannoun; Pietro Marco Congedo (J. Comput. Phys., 2019)

Surrogate-assisted Bounding-Box approach for optimization problems with tunable objectives fidelity. - Mickael Rivier; Pietro Marco Congedo (J. Glob. Optim., 2019)

A surrogate-based optimal likelihood function for the Bayesian calibration of catalytic recombination in atmospheric entry protection materials. - Anabel del Val; Olivier P. Le Maître; Olivier Chazot; Thierry E. Magin; Pietro Marco Congedo (CoRR, 2020)

Forward and backward uncertainty quantification with active subspaces: Application to hypersonic flows around a cylinder. - Andrea F. Cortesi; Paul G. Constantine; Thierry E. Magin; Pietro Marco Congedo (J. Comput. Phys., 2020)

Extension of AK-MCS for the efficient computation of very small failure probabilities. - Nassim Razaaly; Pietro Marco Congedo (Reliab. Eng. Syst. Saf., 2020)

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

Assessment of a Non-Conservative Four-Equation Multiphase System with Phase Transition. - Paola Bacigaluppi; Julien Carlier; Marica Pelanti; Pietro Marco Congedo; Rémi Abgrall (J. Sci. Comput., 2022)

Surrogate-Assisted Bounding-Box approach applied to constrained multi-objective optimisation under uncertainty. - Mickael Rivier; Pietro Marco Congedo (Reliab. Eng. Syst. Saf., 2022)

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

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)