50 citations · 170 across the 9 of their papers we have counts for
9 papers
Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates
Qiyao Zhou, Xujia Zhu, Pierre Joli +2
Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibrati…
Uncertainty functionals revisited: Concavity and Jensen's inequality
Julien Bect, Xujia Zhu
This article presents a theoretical study of uncertainty functionals on general measurable spaces. These functionals are fundamental in experimental design and global sensitivity a…
MF-GLaM: A multifidelity stochastic emulator using generalized lambda models
K. Giannoukou, X. Zhu, S. Marelli +1
Stochastic simulators exhibit intrinsic stochasticity due to unobservable, uncontrollable, or unmodeled input variables, resulting in random outputs even at fixed input conditions.…
Maximum Likelihood for Logistic Regression Model with Incomplete and Hybrid-Type Covariates
Mohamed Cherifi, Xujia Zhu, Mohammed Nabil El Korso +1
Logistic regression is a fundamental and widely used statistical method for modeling binary outcomes based on covariates. However, the presence of missing data, particularly in set…
Seismic fragility analysis using stochastic polynomial chaos expansions
X. Zhu, M. Broccardo, B. Sudret
Within the performance-based earthquake engineering (PBEE) framework, the fragility model plays a pivotal role. Such a model represents the probability that the engineering demand…
Stochastic polynomial chaos expansions to emulate stochastic simulators
X. Zhu, B. Sudret
In the context of uncertainty quantification, computational models are required to be repeatedly evaluated. This task is intractable for costly numerical models. Such a problem tur…
Emulation of stochastic simulators using generalized lambda models
X. Zhu, B. Sudret
Stochastic simulators are ubiquitous in many fields of applied sciences and engineering. In the context of uncertainty quantification and optimization, a large number of simulation…
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
X. Zhu, B. Sudret
Global sensitivity analysis aims at quantifying the impact of input variability onto the variation of the response of a computational model. It has been widely applied to determini…
Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions
X. Zhu, B. Sudret
Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task. This is exacerbated in the context o…