Track irregularities stochastic modeling G Perrin, C Soize, D Duhamel, C Funfschilling Probabilistic Engineering Mechanics 34, 123-130, 2013 | 91 | 2013 |
Group kernels for Gaussian process metamodels with categorical inputs O Roustant, E Padonou, Y Deville, A Clément, G Perrin, J Giorla, H Wynn SIAM/ASA Journal on Uncertainty Quantification 8 (2), 775-806, 2020 | 70 | 2020 |
Identification of polynomial chaos representations in high dimension from a set of realizations G Perrin, C Soize, D Duhamel, C Funfschilling SIAM Journal on Scientific Computing 34 (6), A2917-A2945, 2012 | 68 | 2012 |
Karhunen–Loève expansion revisited for vector-valued random fields: Scaling, errors and optimal basis. G Perrin, C Soize, D Duhamel, C Funfschilling Journal of Computational Physics 242, 607-622, 2013 | 47 | 2013 |
3D particle shape modelling and optimization through proper orthogonal decomposition: Application to Railway Ballast N Ouhbi, C Voivret, G Perrin, JN Roux Granular Matter 19, 1-14, 2017 | 42 | 2017 |
Quantification of the influence of the track geometry variability on the train dynamics G Perrin, D Duhamel, C Soize, C Funfschilling Mechanical Systems and Signal Processing 60, 945-957, 2015 | 41 | 2015 |
Efficient evaluation of reliability-oriented sensitivity indices G Perrin, G Defaux Journal of Scientific Computing 79 (3), 1433-1455, 2019 | 36 | 2019 |
Active learning surrogate models for the conception of systems with multiple failure modes G Perrin Reliability Engineering & System Safety 149, 130-136, 2016 | 36 | 2016 |
Efficient sequential experimental design for surrogate modeling of nested codes S Marque-Pucheu, G Perrin, J Garnier ESAIM: Probability and Statistics 23, 245-270, 2019 | 34 | 2019 |
Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints G Perrin, C Soize, N Ouhbi Computational Statistics & Data Analysis 119, 139-154, 2018 | 32 | 2018 |
High-speed train suspension health monitoring using computational dynamics and acceleration measurements D Lebel, C Soize, C Funfschilling, G Perrin Vehicle System Dynamics, 2019 | 31 | 2019 |
Boosted optimal weighted least-squares C Haberstich, A Nouy, G Perrin Mathematics of Computation 91 (335), 1281-1315, 2022 | 30 | 2022 |
Railway ballast: grain shape characterization to study its influence on the mechanical behaviour N Ouhbi, C Voivret, G Perrin, JN Roux Procedia engineering 143, 1120-1127, 2016 | 29 | 2016 |
A posteriori error and optimal reduced basis for stochastic processes defined by a finite set of realizations G Perrin, C Soize, D Duhamel, C Funfschilling SIAM/ASA Journal on Uncertainty Quantification 2 (1), 745-762, 2014 | 29 | 2014 |
Propagation of variability in railway dynamic simulations: application to virtual homologation C Funfschilling, G Perrin, S Kraft Vehicle System Dynamics 50 (sup1), 245-261, 2012 | 28 | 2012 |
Adaptive calibration of a computer code with time-series output G Perrin Reliability engineering & system safety 196, 106728, 2020 | 25 | 2020 |
Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics C Soize, C Desceliers, J Guilleminot, TT Le, MT Nguyen, G Perrin, ... (Plenary Lecture) UNCECOMP 2015, 1st ECCOMAS Thematic International …, 2015 | 21 | 2015 |
Probabilistic simulation for the certification of railway vehicles C Funfschilling, G Perrin, M Sebes, Y Bezin, L Mazzola, ML Nguyen-Tajan Proceedings of the Institution of Mechanical Engineers, Part F: Journal of …, 2015 | 20 | 2015 |
A comparison of mixed-variables Bayesian optimization approaches J Cuesta Ramirez, R Le Riche, O Roustant, G Perrin, C Durantin, A Glière Advanced Modeling and Simulation in Engineering Sciences 9 (1), 6, 2022 | 18 | 2022 |
Uncertainty quantification in vehicle dynamics C Funfschilling, G Perrin Vehicle system dynamics 57 (7), 1062-1086, 2019 | 16 | 2019 |