B. Sudret

Bruno Sudret, Jean-Marc Bourinet, Maliki Moustapha, 2018, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.

Rare Event Estimation Using Polynomial-Chaos Kriging

Stefano Marelli, Bruno Sudret, Roland Schöbi, 2017 .

RELIABILITY-BASED DESIGN OPTIMIZATION OF SHELLS WITH UNCERTAIN GEOMETRY USING ADAPTIVE KRIGING METAMODELS

B. Sudret, V. Dubourg, J.-M. Bourinet, 2011, 1104.3479.

Fusing Simulation Results From Multifidelity Aero-servo-elastic Simulators - Application To Extreme Loads On Wind Turbine

John Dalsgaard Sørensen, Bruno Sudret, Anand Natarajan, 2015 .

Polynomial-Chaos-based Kriging

J. Wiart, B. Sudret, R. Schoebi, 2015, 1502.03939.

Meta-model-based importance sampling for reliability sensitivity analysis

Bruno Sudret, V. Dubourg, B. Sudret, 2014 .

METAMODELING FOR CRASHWORTHINESS DESIGN: COMPARATIVE STUDY OF KRIGING AND SUPPORT VECTOR REGRESSION

Bruno Sudret, Jean-Marc Bourinet, Maliki Moustapha, 2014 .

Combining polynomial chaos expansions and Kriging for solving structural reliability problems

Bruno Sudret, Roland Schöbi, R. Schöbi, 2014 .

Uncertainty Quantification in Engineering

Bruno Sudret, B. Sudret, 2013 .

Global sensitivity analysis for models with correlated input parameters

B. Sudret, Y. Caniou, A. Micol, 2012 .

Metamodel-based importance sampling for structural reliability analysis

B. Sudret, V. Dubourg, F. Deheeger, 2011, 1105.0562.

Metamodel-based importance sampling for the simulation of rare events

B. Sudret, V. Dubourg, F. Deheeger, 2011, 1104.3476.

A hierarchical surrogate-based strategy for reliability-based design optimization

Bruno Sudret, V. Dubourg, Jean-Marc Bourinet, 2010 .

Quasi-random numbers in stochastic finite element analysis

Bruno Sudret, Géraud Blatman, M. Berveiller, 2007 .

Structural reliability analysis for p-boxes using multi-level meta-models

B. Sudret, R. Schobi, R. Schöbi, 2017, 1705.03947.

UQLab: a framework for uncertainty quantification in MATLAB

Stefano Marelli, Bruno Sudret, S. Marelli, 2014 .

Gaussian process modelling using UQLab

Stefano Marelli, Bruno Sudret, Christos Lataniotis, 2017 .

The Gaussian Process modelling module in UQLab

S. Marelli, B. Sudret, C. Lataniotis, 2017, 1709.09382.

Rare event estimation using stochastic spectral embedding

Paul Wagner, S. Marelli, I. Papaioannou, 2021, Structural Safety.

Active learning for structural reliability: Survey, general framework and benchmark

S. Marelli, B. Sudret, M. Moustapha, 2021, Structural Safety.

SEQUENTIAL ACTIVE LEARNING OF LOW-DIMENSIONAL MODEL

I. Papaioannou, D. Štraub, B. Sudret, 2021 .

Euclid preparation: II. The EuclidEmulator – a tool to compute the cosmology dependence of the nonlinear matter power spectrum

Stefano Marelli, Bruno Sudret, Joachim Stadel, 2018, Monthly Notices of the Royal Astronomical Society.

Probabilistic LCA and LCC to identify robust and reliable renovation strategies

Guillaume Habert, A Galimshina, A Hollberg, 2019, IOP Conference Series: Earth and Environmental Science.

Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation

J. Rieckermann, B. Sudret, J. Nagel, 2017, Reliab. Eng. Syst. Saf..

Non linear non intrusive stochastic finite element method-A pplication to a fracture mechanics problem

Bruno Sudret, Maurice Lemaire, M. Lemaire, 2004 .

Application of a stochastic finite element procedure to reliability analysis

B. Sudret, M. Berveiller, M. Lemaire, 2020 .

Polynomial chaos expansion for uncertainty quantification of dam engineering problems

Bruno Sudret, Mohammad Amin Hariri-Ardebili, M. A. Hariri-Ardebili, 2020 .

SURROGATE MODELING OF INDOOR DOWN-LINK HUMAN EXPOSURE BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION

Joe Wiart, Bruno Sudret, Dominique Lesselier, 2019, International Journal for Uncertainty Quantification.

Sequential piecewise PCE approximation of likelihood functions in Bayesian inference

Stefano Marelli, Bruno Sudret, Paul-Remo Wagner, 2019 .

Uncertainty propagation of a multiscale poromechanics-hydration model for poroelastic properties of cement paste at early-age

L. Sorelli, B. Sudret, N. Venkovic, 2013 .

A two-phase elastoplastic model for unidirectionally-reinforced materials

Bruno Sudret, Patrick de Buhan, B. Sudret, 1999 .

Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models

Bruno Sudret, Xujia Zhu, B. Sudret, 2019, Reliab. Eng. Syst. Saf..

Extending classical surrogate modelling to ultrahigh dimensional problems through supervised dimensionality reduction: a data-driven approach pdf

Stefano Marelli, Bruno Sudret, C. Lataniotis, 2018, ArXiv.

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

B. Sudret, A. T. Beck, H. M. Kroetz, 2020, Reliab. Eng. Syst. Saf..

Reliability-based design optimization using kriging surrogates and subset simulation

B. Sudret, V. Dubourg, J.-M. Bourinet, 2011, 1104.3667.

3-D Spectral Stochastic Finite Element Method in Electromagnetism

B. Sudret, O. Moreau, S. Clenet, 2006, IEEE Transactions on Magnetics.

Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions

Bruno Sudret, Katerina Konakli, B. Sudret, 2015, J. Comput. Phys..

Bayesian model inversion using stochastic spectral embedding

Paul Wagner, S. Marelli, B. Sudret, 2020, J. Comput. Phys..

Statistical method to identify robust building renovation choices for environmental and economic performance

Sébastien Lasvaux, Guillaume Habert, Bruno Sudret, 2020, Building and Environment.

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

B. Sudret, G. Habert, A. Galimshina, 2022, Structural and Multidisciplinary Optimization.

What is the optimal robust environmental and cost-effective solution for building renovation? Not the usual one

Bruno Sudret, Sébastien Lasvaux, Guillaume Habert, 2021 .

Effective Design for Sobol Indices Estimation Based on Polynomial Chaos Expansions

Bruno Sudret, Evgeny Burnaev, Ivan Panin, 2016, COPA.

Sequential Design of Experiment for Sparse Polynomial Chaos Expansions

Stefano Marelli, Bruno Sudret, Noura Fajraoui, 2017, SIAM/ASA J. Uncertain. Quantification.

Updating the long-term creep strains in concrete containment vessels by using Markov chain Monte Carlo simulation and polynomial chaos expansions

Bruno Sudret, Marc Berveiller, Frédéric Perrin, 2012 .

Use of polynomial chaos expansions and maximum likelihood estimation for probabilistic inverse problems

Bruno Sudret, Géraud Blatman, Maurice Pendola, 2007 .

Efficient computation of global sensitivity indices using sparse polynomial chaos expansions

Bruno Sudret, Géraud Blatman, B. Sudret, 2010, Reliab. Eng. Syst. Saf..

An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis

S. Marelli, B. Sudret, S. Marelli, 2017, Structural Safety.

A new system formulation for the tolerance analysis of overconstrained mechanisms

Nicolas Gayton, Jean-Yves Dantan, Bruno Sudret, 2015 .

Surrogate models for uncertainty quantification: An overview

Joe Wiart, Stefano Marelli, Bruno Sudret, 2017, 2017 11th European Conference on Antennas and Propagation (EUCAP).

Physical and Polynomial Response Surfaces

F. Schoefs, B. Sudret, F. Duprat, 2013 .

Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions

Bruno Sudret, Evgeny Burnaev, Ivan Panin, 2017, Annals of Mathematics and Artificial Intelligence.

Global sensitivity analysis using low-rank tensor approximations

Bruno Sudret, Katerina Konakli, B. Sudret, 2016, Reliab. Eng. Syst. Saf..

Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model

Bruno Sudret, G. Deman, J. Kerrou, 2015, Reliab. Eng. Syst. Saf..

Computing derivative-based global sensitivity measures using polynomial chaos expansions

Bruno Sudret, Chu V. Mai, B. Sudret, 2014, Reliab. Eng. Syst. Saf..

Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions

Bruno Sudret, Roland Schöbi, R. Schöbi, 2017, Reliab. Eng. Syst. Saf..

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

Bruno Sudret, Roland Schöbi, R. Schöbi, 2016, J. Comput. Phys..

Hamiltonian Monte Carlo and Borrowing Strength in Hierarchical Inverse Problems

B. Sudret, J. Nagel, 2016 .

Numerical Investigations into the Value of Information in Lifecycle Analysis of Structural Systems

Michael Havbro Faber, Bruno Sudret, Katerina Konakli, 2016 .

A unified framework for multilevel uncertainty quantification in Bayesian inverse problems

B. Sudret, J. Nagel, 2016 .

Bayesian Multilevel Model Calibration for Inverse Problems Under Uncertainty with Perfect Data

Bruno Sudret, Joseph B. Nagel, B. Sudret, 2015, J. Aerosp. Inf. Syst..

Surrogate modeling for stochastic dynamical systems by combining nonlinear autoregressive with exogenous input models and polynomial chaos expansions

Bruno Sudret, Eleni Chatzi, Minas Spiridonakos, 2016 .

Characterization of random stress fields obtained from polycrystalline aggregate calculations using multi-scale stochastic finite elements

M. Berveiller, B. Sudret, T. Yalamas, 2015, Frontiers of Structural and Civil Engineering.

Introducing stress random fields of polycrystalline aggregates into the local approach to fracture

M. Berveiller, B. Sudret, T. Yalamas, 2013 .

Reliability analysis of high-dimensional models using low-rank tensor approximations

B. Sudret, K. Konakli, B. Sudret, 2016, 1606.08577.

Hierarchical adaptive polynomial chaos expansions

Bruno Sudret, Chu V. Mai, B. Sudret, 2015, 1506.00461.

Bayesian updating of mechanical models - Application in fracture mechanics

Bruno Sudret, Maurice Pendola, Frédéric Perrin, 2007 .

A TWO-STAGE SURROGATE MODELING APPROACH FOR THE APPROXIMATION OF MODELS WITH NON-SMOOTH OUTPUTS

Bruno Sudret, Maliki Moustapha, B. Sudret, 2019, Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019).

A spectral surrogate model for stochastic simulators computed from trajectory samples

S. Marelli, B. Sudret, N. Lüthen, 2022, Computer Methods in Applied Mechanics and Engineering.

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

Irena Hajnsek, Onur Yuzugullu, Stefano Marelli, 2017, Remote. Sens..

Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels

B. Sudret, P.-R. Wagner, R. Fahrni, 2019, Engineering Structures.

Computing seismic fragility curves using non-parametric representations

B. Sudret, C. Mai, K. Konakli, 2014 .

Surrogate model uncertainty in wind turbine reliability assessment

John Dalsgaard Sørensen, Bruno Sudret, Lasse Svenningsen, 2020, Renewable Energy.

Polynomial chaos expansion models for the monitoring of structures under operational variability

Eleni Chatzi, Minas Spiridonakos, Bruno Sudret, 2016 .

Stochastic finite element methods in geotechnical engineering

Bruno Sudret, M. Berveiller, B. Sudret, 2008 .

The PHI2 method: a way to compute time-variant reliability

Bruno Sudret, Maurice Lemaire, C. Andrieu-Renaud, 2004, Reliab. Eng. Syst. Saf..

Uncertainties in the risk assessment of hydropower dams:state-of-the-art and outlook

Peter Burgherr, Matteo Spada, A. Kalinina, 2016 .

How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model ? Application to the PESHMELBA model How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model ? Application to the PESHMELBA model.

A. Vidard, B. Sudret, C. Lauvernet, 2021 .

Life-time reliability based assessment of structures submitted to thermal fatigue

Bruno Sudret, Maurice Lemaire, Z. Guédé, 2007 .

Sparse polynomial chaos expansions of vector-valued response quantities

Bruno Sudret, Géraud Blatman, B. Sudret, 2014 .

Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation

S. Marelli, B. Sudret, V. Yaghoubi, 2016, 1606.01662.

Surrogate Models for Oscillatory Systems Using Sparse Polynomial Chaos Expansions and Stochastic Time Warping

Bruno Sudret, Chu V. Mai, B. Sudret, 2016, SIAM/ASA J. Uncertain. Quantification.

Combining Polynomial Chaos Expansions and Kriging

Joe Wiart, Bruno Sudret, Roland Schöbi, 2014 .

Combining machine learning and surrogate modeling for data-driven uncertainty propagation in high-dimension

Stefano Marelli, Bruno Sudret, Christos Lataniotis, 2019 .

A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas

E. Torre, S. Marelli, P. Embrechts, 2017, Probabilistic Engineering Mechanics.

Polynomial-Chaos-Kriging

Bruno Sudret, Roland Schöbi, R. Schöbi, 2014 .

PC-Kriging: A new meta-modelling method and its application to quantile estimation

Bruno Sudret, Roland Schöbi, R. Schöbi, 2014 .

A TWO-STAGE SURROGATE MODELING APPROACH FOR THE APPROXIMATION OF MODELS WITH NON-SMOOTH OUTPUTS

Bruno Sudret, Maliki Moustapha, B. Sudret, 2019, Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019).

Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation

Bruno Sudret, Joseph B. Nagel, Jorg Rieckermann, 2017, Reliability Engineering & System Safety.

Principal component analysis and Least Angle Regression in spectral stochastic finite element analysis

Bruno Sudret, Géraud Blatman, B. Sudret, 2011 .

Analytical derivation of the outcrossing rate in time-variant reliability problems

Bruno Sudret, B. Sudret, 2008 .

Stochastic spectral embedding for Bayesian inverse problems

Stefano Marelli, Bruno Sudret, Paul-Remo Wagner, 2019 .

How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model

A. Vidard, B. Sudret, C. Lauvernet, 2023, Geoscientific Model Development.

Sparse Polynomial Chaos Expansions as a Machine Learning Regression Technique

Bruno Sudret, B. Sudret, 2015 .

Uncertainty Quantification and Global Sensitivity Analysis for Economic Models

Stefano Marelli, Bruno Sudret, Viktor Winschel, 2017, Quantitative Economics.

Stochastic evaluation of the damage length in RC beams submitted to corrosion of reinforcing steel

Bruno Sudret, G. Defaux, M. Pendola, 2007 .

Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis

S. Marelli, B. Sudret, M. Moustapha, 2023, 2305.19885.

Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks

Calvin A. Whealton, Stefano Marelli, Bruno Sudret, 2020, Energies.

The UQLab project: steps toward a global uncertainty quantification community

Stefano Marelli, S. Marelli, B. Sudret, 2019 .

SURROGATE MODELING OF STOCHASTIC FUNCTIONS-APPLICATION TO COMPUTATIONAL ELECTROMAGNETIC DOSIMETRY

Joe Wiart, Bruno Sudret, Yuanyuan Huang, 2018, International Journal for Uncertainty Quantification.

Hierarchical Kriging for multi-fidelity aero-servo-elastic simulators - Application to extreme loads on wind turbines

C. Lataniotis, B. Sudret, I. Abdallah, 2017, 1709.07637.

Imprecise structural reliability analysis using PC-Kriging

Bruno Sudret, Roland Schöbi, R. Schöbi, 2015 .

Propagation of Uncertainties Modelled by Parametric P-boxes Using Sparse Polynomial Chaos Expansions

Bruno Sudret, Roland Schöbi, R. Schöbi, 2015 .

A Novel Seismic Structural Testing Protocol Based on Hybrid Simulation, Kriging and Active Learning: Methodology and Numerical Examples

Stefano Marelli, Bruno Sudret, Giuseppe Abbiati, 2019 .

Multi-level meta-modelling in imprecise structural reliability analysis

Bruno Sudret, Roland Schöbi, R. Schöbi, 2016 .

Bootstrap-Polynomial Chaos Expansions and Adaptive Designs for Reliability Analysis

Stefano Marelli, Bruno Sudret, S. Marelli, 2016 .

Adaptive Kriging reliability-based design optimization of an automotive body structure under crashworthiness constraints

Bruno Sudret, Jean-Marc Bourinet, Maliki Moustapha, 2015 .

Advanced computational methods for structural reliability analysis - applications in civil engineering

Stefano Marelli, Bruno Sudret, S. Marelli, 2015 .

Increasing contribution of stochastic methods in the characterization of complex systems: Reverberation chamber and urban environment

J. Wiart, O. Picon, B. Sudret, 2013, International Conference on Electromagnetics in Advanced Applications.

Surrogate modeling based on resampled polynomial chaos expansions

Joe Wiart, Bruno Sudret, Zicheng Liu, 2018, Reliab. Eng. Syst. Saf..

Organizational factors and safety culture

E. Zio, W. Kröger, L. Podofillini, 2015 .

Development of Probabilistic Dam Breach Model Using Bayesian Inference

S. Marelli, B. Sudret, J. Nagel, 2018, Water Resources Research.

A Bayesian Multilevel Framework for Uncertainty Characterization and the NASA Langley Multidisciplinary UQ Challenge

Bruno Sudret, Joseph B. Nagel, B. Sudret, 2014 .

Application of Conditional Random Fields and Sparse Polynomial Chaos Expansions to Geotechnical Problems

Bruno Sudret, Roland Schöbi, R. Schöbi, 2015 .

Probabilistic models for the extent of damage in degrading reinforced concrete structures

Bruno Sudret, B. Sudret, 2008, Reliab. Eng. Syst. Saf..

Multiphase model for inclusion‐reinforced geostructures: Application to rock‐bolted tunnels and piled raft foundations

Bruno Sudret, Patrick de Buhan, B. Sudret, 2001 .

Emulation of Stochastic Simulators Using Generalized Lambda Models

B. Sudret, Xujia Zhu, 2020, SIAM/ASA J. Uncertain. Quantification.

REPLICATION-BASED EMULATION OF THE RESPONSE DISTRIBUTION OF STOCHASTIC SIMULATORS USING GENERALIZED LAMBDA DISTRIBUTIONS

B. Sudret, X. Zhu, B. Sudret, 2019, International Journal for Uncertainty Quantification.

Uncertainty quantification in high-dimensional spaces with low-rank tensor approximations

Bruno Sudret, Katerina Konakli, B. Sudret, 2015 .

Bayesian model inversion using stochastic spectral embedding

S. Marelli, B. Sudret, P.-R. Wagner, 2020, J. Comput. Phys..

REPLICATION-BASED EMULATION OF THE RESPONSE DISTRIBUTION OF STOCHASTIC SIMULATORS USING GENERALIZED LAMBDA DISTRIBUTIONS

B. Sudret, Xujia Zhu, 2019, International Journal for Uncertainty Quantification.

Development and Realization of Validation Benchmarks

R. Helmig, B. Sudret, 2021 .

Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions

F. Gamboa, B. Iooss, S. Marelli, 2021, International Journal for Uncertainty Quantification.

ON OPTIMAL EXPERIMENTAL DESIGNS FOR SPARSE POLYNOMIAL CHAOS EXPANSIONS

S. Marelli, B. Sudret, N. Fajraoui, 2017, 1703.05312.

Data-driven polynomial chaos expansions for machine learning regression

B. Sudret, E. Torre, S. Marelli, 2020 .

PC-Kriging: A new meta-modelling method and its application to quantile estimation

R. Schöbi, B. Sudret, 2014 .

Computing seismic fragility curves using polynomial chaos expansions

Chu V. Mai, Bruno Sudret, B. Sudret, 2013 .

Global sensitivity analysis has emerged in the last 15 years as a powerful tool for improving the understanding of complex environmental, industrial or civil systems. In many

Bruno Sudret, Chu V. Mai, B. Sudret, 2013 .

Sixth International Conference on Sensitivity Analysis of Model Output A comparison of three metamodel-based methods for global sensitivity analysis: GP modelling, HDMR and LAR-gPC

B. Sudret, Géraud Blatman, 2010 .

Sparse polynomial chaos expansions based on an adaptive Least Angle Regression algorithm

B. Sudret, Géraud Blatman, 2009 .

Analysis of covariance (ANCOVA) using polynomial chaos expansions

Bruno Sudret, Yves Caniou, Y. Caniou, 2013 .

Uncertainty Quantification and Global Sensitivity Analysis for Economic Models

S. Marelli, B. Sudret, Daniel Harenberg, 2017, Quantitative Economics.

How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model

A. Vidard, B. Sudret, C. Lauvernet, 2021 .

Robust and resilient renovation solutions in different climate change scenarios

B. Sudret, G. Habert, P. Padey, 2020, IOP Conference Series: Earth and Environmental Science.