| UQLab: a Framework for Uncertainty Quantification in Matlab S Marelli, B Sudret Second International Conference on Vulnerability and Risk Analysis and …, 2014 | 1018 | 2014 |
| UQLab user manual–Polynomial chaos expansions S Marelli, B Sudret Chair of risk, safety & uncertainty quantification, ETH Zürich, 0.9-104 …, 2015 | 297 | 2015 |
| Rare event estimation using polynomial-chaos kriging R Schöbi, B Sudret, S Marelli ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A …, 2017 | 285 | 2017 |
| An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis S Marelli, B Sudret Structural Safety 75, 67-74, 2018 | 243 | 2018 |
| Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes L Le Gratiet, S Marelli, B Sudret Handbook of Uncertainty Quantification, 2016 | 219 | 2016 |
| Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe T Tröndle, J Lilliestam, S Marelli, S Pfenninger Joule 4 (9), 1929-1948, 2020 | 188 | 2020 |
| Sparse polynomial chaos expansions: Literature survey and benchmark N Lüthen, S Marelli, B Sudret SIAM/ASA Journal on Uncertainty Quantification 9 (2), 593-649, 2021 | 186 | 2021 |
| UQLab user manual – Kriging C Lataniotis, S Marelli, B Sudret Technical report, Chair of risk, safety & uncertainty quantification, ETH …, 2015 | 177* | 2015 |
| Surrogate models for uncertainty quantification: An overview B Sudret, S Marelli, J Wiart 2017 11th European conference on antennas and propagation (EUCAP), 793-797, 2017 | 169 | 2017 |
| Euclid preparation: II. The EuclidEmulator – a tool to compute the cosmology dependence of the nonlinear matter power spectrum Euclid Collaboration, M Knabenhans, J Stadel, S Marelli, D Potter, ... Monthly Notices of the Royal Astronomical Society 484 (4), 5509-5529, 2019 | 156 | 2019 |
| Data-driven polynomial chaos expansion for machine learning regression E Torre, S Marelli, P Embrechts, B Sudret Journal of Computational Physics 388, 601-623, 2019 | 149 | 2019 |
| Active learning for structural reliability: Survey, general framework and benchmark M Moustapha, S Marelli, B Sudret Structural Safety 96, 102174, 2022 | 134 | 2022 |
| Euclid preparation: IX. EuclidEmulator2 – power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations Euclid Collaboration, M Knabenhans, J Stadel, D Potter, J Dakin, ... Monthly Notices of the Royal Astronomical Society 505 (2), 2840-2869, 2021 | 117 | 2021 |
| A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas E Torre, S Marelli, P Embrechts, B Sudret Probabilistic Engineering Mechanics 55, 1-16, 2019 | 113 | 2019 |
| UQLab user manual - Sensitivity Analysis S Marelli, C Lamas-Fernandes, B Sudret Tech. Rep, 2015 | 112* | 2015 |
| Engineering analysis with probability boxes: A review on computational methods MGR Faes, M Daub, S Marelli, E Patelli, M Beer Structural Safety 93, 102092, 2021 | 99 | 2021 |
| Sequential design of experiment for sparse polynomial chaos expansions N Fajraoui, S Marelli, B Sudret SIAM/ASA Journal on Uncertainty Quantification 5 (1), 1061-1085, 2017 | 96* | 2017 |
| Extending classical surrogate modelling to high dimensions through supervised dimensionality reduction: a data-driven approach C Lataniotis, S Marelli, B Sudret International Journal for Uncertainty Quantification 10 (1), 55-82, 2020 | 80 | 2020 |
| Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation V Yaghoubi, S Marelli, B Sudret, T Abrahamsson Probabilistic engineering mechanics 48, 39-58, 2017 | 67 | 2017 |
| UQLab user manual–The input module C Lataniotis, S Marelli, B Sudret Report UQLab-V0, 9-102, 2015 | 54 | 2015 |
Bruno SudretETH Zurich在 ethz.ch 的电子邮件经过验证
