[HTML][HTML] A spectral surrogate model for stochastic simulators computed from trajectory samples
Stochastic simulators are non-deterministic computer models which provide a different
response each time they are run, even when the input parameters are held at fixed values …
response each time they are run, even when the input parameters are held at fixed values …
SENSITIVITY ANALYSES OF A MULTIPHYSICS LONG-TERM CLOGGING MODEL FOR STEAM GENERATORS
Long-term operation of nuclear steam generators can result in the occurrence of clogging, a
deposition phenomenon that may increase the risk of mechanical and vibration loadings on …
deposition phenomenon that may increase the risk of mechanical and vibration loadings on …
[PDF][PDF] Sensitivity Analyses of a Multi-Physics Long-Term Clogging Model For Steam Generators
V Chabridon, E Jaber, E Remy, M Baudin… - International Journal …, 2024 - researchgate.net
Long-term operation of nuclear steam generators can result in the occurrence of clogging, a
deposition phenomenon that may increase the risk of mechanical and vibration loadings on …
deposition phenomenon that may increase the risk of mechanical and vibration loadings on …
On one dimensional weighted Poincare inequalities for Global Sensitivity Analysis
D Heredia, A Joulin, O Roustant - arXiv preprint arXiv:2412.04918, 2024 - arxiv.org
One-dimensional Poincare inequalities are used in Global Sensitivity Analysis (GSA) to
provide derivative-based upper bounds and approximations of Sobol indices. We add new …
provide derivative-based upper bounds and approximations of Sobol indices. We add new …
Spectral decomposition of H1 (μ) and Poincaré inequality on a compact interval—Application to kernel quadrature
Motivated by uncertainty quantification of complex systems, we aim at finding quadrature
formulas of the form∫ abf (x) d μ (x)=∑ i= 1 nwif (xi) where f belongs to H 1 (μ). Here, μ …
formulas of the form∫ abf (x) d μ (x)=∑ i= 1 nwif (xi) where f belongs to H 1 (μ). Here, μ …
Stochastic Spectral Embedding in Forward and Inverse Uncertainty Quantification
PR Wagner - 2021 - research-collection.ethz.ch
Uncertainties are an important ingredient in the analysis of real-world systems by means of
computational models. The scientific discipline that develops methods for modelling …
computational models. The scientific discipline that develops methods for modelling …
Bayesian quadrature for with Poincar\'e inequality on a compact interval
Motivated by uncertainty quantification of complex systems, we aim at finding quadrature
formulas of the form $\int_a^ bf (x) d\mu (x)=\sum_ {i= 1}^ n w_i f (x_i) $ where $ f $ belongs …
formulas of the form $\int_a^ bf (x) d\mu (x)=\sum_ {i= 1}^ n w_i f (x_i) $ where $ f $ belongs …
Bayesian³ Active learning for regularized arbitrary multi-element polynomial chaos using information theory
I Kröker, T Brünnette, N Wildt… - … Journal for Uncertainty … - dl.begellhouse.com
Machine learning, surrogate modeling, and uncertainty quantification pose challenges in
data-poor applications that arise due to limited availability of measurement data or with …
data-poor applications that arise due to limited availability of measurement data or with …
Surrogate modeling for stochastic simulators using statistical approaches
X Zhu - 2023 - research-collection.ethz.ch
Nowadays, more and more complex interdependent infrastructures and networks are
developed in engineering. The design and maintenance of such systems increasingly call …
developed in engineering. The design and maintenance of such systems increasingly call …
Sensitivity analysis and algorithmic fairness for machine learning and artificial intelligence
C Benesse - 2022 - theses.hal.science
In recent years, the use of algorithms coming from the Machine Learning literature has
skyrocketed. Always seeking better performances at the price of more and more data, these …
skyrocketed. Always seeking better performances at the price of more and more data, these …