A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes
Uncertainty Quantification (UQ) is an essential step in computational model validation
because assessment of the model accuracy requires a concrete, quantifiable measure of …
because assessment of the model accuracy requires a concrete, quantifiable measure of …
Spatial statistical models: An overview under the Bayesian approach
Spatial documentation is exponentially increasing given the availability of Big Data in the
Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian …
Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian …
Reliability analysis of hydrogen storage composite pressure vessel with two types of random-interval uncertainties
W Li, L Zhang, H Lv, L Zhang, M Liu, C Zhang… - International Journal of …, 2023 - Elsevier
Due to the uncertainty of structure and material parameters, reliability analysis of hydrogen
storage composite pressure vessel (CPV) is an important issue. In this paper, reliability …
storage composite pressure vessel (CPV) is an important issue. In this paper, reliability …
Influence of structural modifications of automotive brake systems for squeal events with kriging meta-modelling method
Squeal noise is an important issue in the automotive industry since it is one of the main
reasons for the return of vehicles to the customer service. Hence, it is essential to predict it in …
reasons for the return of vehicles to the customer service. Hence, it is essential to predict it in …
A Gaussian process emulator based approach for Bayesian calibration of a functional input
Bayesian calibration of a functional input/parameter to a time-consuming simulator based on
a Gaussian process (GP) emulator involves two challenges that distinguish it from other …
a Gaussian process (GP) emulator involves two challenges that distinguish it from other …
A review on quantile regression for stochastic computer experiments
We report on an empirical study of the main strategies for quantile regression in the context
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …
Efficient sequential experimental design for surrogate modeling of nested codes
In this paper we consider two nested computer codes, with the first code output as one of the
second code inputs. A predictor of this nested code is obtained by coupling the Gaussian …
second code inputs. A predictor of this nested code is obtained by coupling the Gaussian …
On the inference of applying Gaussian process modeling to a deterministic function
W Wang - Electronic Journal of Statistics, 2021 - projecteuclid.org
Gaussian process modeling is a standard tool for building emulators for computer
experiments, which are usually used to study deterministic functions, for example, a solution …
experiments, which are usually used to study deterministic functions, for example, a solution …
Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case
F Bachoc - 2018 - projecteuclid.org
Asymptotic analysis of covariance parameter estimation for Gaussian processes in the
misspecified case Page 1 Bernoulli 24(2), 2018, 1531–1575 DOI: 10.3150/16-BEJ906 …
misspecified case Page 1 Bernoulli 24(2), 2018, 1531–1575 DOI: 10.3150/16-BEJ906 …
Sampling, metamodeling, and sensitivity analysis of numerical simulators with functional stochastic inputs
In this paper, we define a new methodology to perform sensitivity analysis of a computer
simulation code in a particular case, whose study is motivated by a nuclear reliability …
simulation code in a particular case, whose study is motivated by a nuclear reliability …