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Bruno Sudret
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2020 – today
- 2024
- [j30]Maliki Moustapha
, Pietro Parisi, Stefano Marelli, Bruno Sudret:
Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis. Reliab. Eng. Syst. Saf. 248: 110150 (2024) - [i11]Felix Schneider, Iason Papaioannou, Bruno Sudret, Gerhard Müller:
Maximum a Posteriori Estimation for Linear Structural Dynamics Models Using Bayesian Optimization with Rational Polynomial Chaos Expansions. CoRR abs/2408.03569 (2024) - [i10]Lucas Brunel, Mathieu Balesdent, Loïc Brevault, Rodolphe Le Riche, Bruno Sudret:
A survey on multi-fidelity surrogates for simulators with functional outputs: unified framework and benchmark. CoRR abs/2408.17075 (2024) - 2023
- [i9]Styfen Schär, Stefano Marelli
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX). CoRR abs/2306.16335 (2023) - 2022
- [j29]Bertrand Iooss
Editorial for the special issue on "sensitivity analysis of model outputs" reliability engineering and system safety. Reliab. Eng. Syst. Saf. 223: 108477 (2022) - [j28]Max Ehre
Sequential Active Learning of Low-Dimensional Model Representations for Reliability Analysis. SIAM J. Sci. Comput. 44(3): 558- (2022) - [i8]Nora Lüthen, Stefano Marelli
A spectral surrogate model for stochastic simulators computed from trajectory samples. CoRR abs/2207.05653 (2022) - [i7]Maliki Moustapha, Bruno Sudret
Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling. CoRR abs/2211.16909 (2022) - 2021
- [j27]Paul-Remo Wagner
Bayesian model inversion using stochastic spectral embedding. J. Comput. Phys. 436: 110141 (2021) - [j26]Nora Lüthen, Stefano Marelli
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark. SIAM/ASA J. Uncertain. Quantification 9(2): 593-649 (2021) - [j25]Xujia Zhu
Emulation of Stochastic Simulators Using Generalized Lambda Models. SIAM/ASA J. Uncertain. Quantification 9(4): 1345-1380 (2021) - [j24]Xujia Zhu
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models. Reliab. Eng. Syst. Saf. 214: 107815 (2021) - [i6]P.-R. Wagner, Stefano Marelli, Iason Papaioannou, Daniel Straub, Bruno Sudret:
Rare event estimation using stochastic spectral embedding. CoRR abs/2106.05824 (2021) - [i5]Nora Lüthen, Olivier Roustant, Fabrice Gamboa, Bertrand Iooss, Stefano Marelli, Bruno Sudret:
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions. CoRR abs/2107.00394 (2021) - 2020
- [j23]Joseph B. Nagel, Jörg Rieckermann, Bruno Sudret
Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation. Reliab. Eng. Syst. Saf. 195: 106737 (2020) - [j22]Zicheng Liu, Dominique Lesselier
Surrogate modeling based on resampled polynomial chaos expansions. Reliab. Eng. Syst. Saf. 202: 107008 (2020) - [j21]Henrique Machado Kroetz
A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems. Reliab. Eng. Syst. Saf. 203: 107033 (2020) - [i4]Nora Lüthen, Stefano Marelli, Bruno Sudret:
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark. CoRR abs/2002.01290 (2020) - [i3]Nora Lüthen, Stefano Marelli, Bruno Sudret:
Sparse Polynomial Chaos Expansions: Solvers, Basis Adaptivity and Meta-selection. CoRR abs/2009.04800 (2020)
2010 – 2019
- 2019
- [j20]Emiliano Torre
Data-driven polynomial chaos expansion for machine learning regression. J. Comput. Phys. 388: 601-623 (2019) - [j19]Roland Schöbi, Bruno Sudret
Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions. Reliab. Eng. Syst. Saf. 187: 129-141 (2019) - 2018
- [j18]Mohammad Reza Moarefzadeh, Bruno Sudret
Implementation of directional simulation to estimate outcrossing rates in time-variant reliability analysis of structures. Qual. Reliab. Eng. Int. 34(8): 1818-1827 (2018) - [i2]Emiliano Torre, Stefano Marelli, Paul Embrechts, Bruno Sudret:
Data-driven polynomial chaos expansion for machine learning regression. CoRR abs/1808.03216 (2018) - [i1]C. Lataniotis, Stefano Marelli, Bruno Sudret:
Extending classical surrogate modelling to ultrahigh dimensional problems through supervised dimensionality reduction: a data-driven approach. CoRR abs/1812.06309 (2018) - 2017
- [j17]Evgeny Burnaev
Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions. Ann. Math. Artif. Intell. 81(1-2): 187-207 (2017) - [j16]Roland Schöbi, Bruno Sudret
Uncertainty propagation of p-boxes using sparse polynomial chaos expansions. J. Comput. Phys. 339: 307-327 (2017) - [j15]Chu V. Mai, Bruno Sudret
Surrogate Models for Oscillatory Systems Using Sparse Polynomial Chaos Expansions and Stochastic Time Warping. SIAM/ASA J. Uncertain. Quantification 5(1): 540-571 (2017) - [j14]Noura Fajraoui, Stefano Marelli
Sequential Design of Experiment for Sparse Polynomial Chaos Expansions. SIAM/ASA J. Uncertain. Quantification 5(1): 1061-1085 (2017) - [j13]Onur Yuzugullu, Stefano Marelli
Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion. Remote. Sens. 9(5): 460 (2017) - 2016
- [j12]Joseph B. Nagel, Bruno Sudret
Spectral likelihood expansions for Bayesian inference. J. Comput. Phys. 309: 267-294 (2016) - [j11]Katerina Konakli
Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions. J. Comput. Phys. 321: 1144-1169 (2016) - [j10]G. Deman, Katerina Konakli
Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model. Reliab. Eng. Syst. Saf. 147: 156-169 (2016) - [j9]Katerina Konakli
Global sensitivity analysis using low-rank tensor approximations. Reliab. Eng. Syst. Saf. 156: 64-83 (2016) - [c2]Evgeny Burnaev
Effective Design for Sobol Indices Estimation Based on Polynomial Chaos Expansions. COPA 2016: 165-184 - 2015
- [j8]Joseph B. Nagel, Bruno Sudret
Bayesian Multilevel Model Calibration for Inverse Problems Under Uncertainty with Perfect Data. J. Aerosp. Inf. Syst. 12(1): 97-113 (2015) - [j7]Pierric Kersaudy, Bruno Sudret
A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry. J. Comput. Phys. 286: 103-117 (2015) - [j6]Bruno Sudret
Computing derivative-based global sensitivity measures using polynomial chaos expansions. Reliab. Eng. Syst. Saf. 134: 241-250 (2015) - [c1]Onur Yuzugullu, Stefano Marelli
Global sensitivity analysis of a morphology based electromagnetic scattering model. IGARSS 2015: 2743-2746 - 2011
- [j5]Géraud Blatman, Bruno Sudret
Adaptive sparse polynomial chaos expansion based on least angle regression. J. Comput. Phys. 230(6): 2345-2367 (2011) - 2010
- [j4]Géraud Blatman, Bruno Sudret
Efficient computation of global sensitivity indices using sparse polynomial chaos expansions. Reliab. Eng. Syst. Saf. 95(11): 1216-1229 (2010)
2000 – 2009
- 2008
- [j3]Bruno Sudret
Probabilistic models for the extent of damage in degrading reinforced concrete structures. Reliab. Eng. Syst. Saf. 93(3): 410-422 (2008) - [j2]Bruno Sudret
Global sensitivity analysis using polynomial chaos expansions. Reliab. Eng. Syst. Saf. 93(7): 964-979 (2008) - 2004
- [j1]C. Andrieu-Renaud, Bruno Sudret
The PHI2 method: a way to compute time-variant reliability. Reliab. Eng. Syst. Saf. 84(1): 75-86 (2004)
Coauthor Index
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