Modeling, analysis, and optimization under uncertainties: a review
Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …
has been an active area of research due to its evident advantages over deterministic design …
Review of statistical model calibration and validation—from the perspective of uncertainty structures
Computer-aided engineering (CAE) is now an essential instrument that aids in engineering
decision-making. Statistical model calibration and validation has recently drawn great …
decision-making. Statistical model calibration and validation has recently drawn great …
Learning of battery model bias for effective state of charge estimation of lithium-ion batteries
State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and
the estimation accuracy was mainly investigated through the development of various battery …
the estimation accuracy was mainly investigated through the development of various battery …
Model-based reliability analysis with both model uncertainty and parameter uncertainty
Z Xi - Journal of Mechanical Design, 2019 - asmedigitalcollection.asme.org
Model-based reliability analysis may not be practically useful if reliability estimation contains
uncontrollable errors. This paper addresses potential reliability estimation errors from model …
uncontrollable errors. This paper addresses potential reliability estimation errors from model …
A model validation framework based on parameter calibration under aleatory and epistemic uncertainty
Abstract Model validation methods have been widely used in engineering design to
evaluate the accuracy and reliability of simulation models with uncertain inputs. Most of the …
evaluate the accuracy and reliability of simulation models with uncertain inputs. Most of the …
Confidence-based reliability assessment considering limited numbers of both input and output test data
Simulation-based methods can be used for accurate uncertainty quantification and
prediction of the reliability of a physical system under the following assumptions:(1) accurate …
prediction of the reliability of a physical system under the following assumptions:(1) accurate …
Confidence-driven design optimization using Gaussian process metamodeling with insufficient data
To reduce the computational cost, surrogate models have been widely used to replace
expensive simulations in design under uncertainty. However, most existing methods may …
expensive simulations in design under uncertainty. However, most existing methods may …
Reliability measure approach for confidence-based design optimization under insufficient input data
In most of the reliability-based design optimization (RBDO) researches, accurate input
statistical model has been assumed to concentrate on the variability of random variables; …
statistical model has been assumed to concentrate on the variability of random variables; …
Adaptive kriging model-based structural reliability analysis under interval uncertainty with incomplete data
P Wu, Y Li - Structural and Multidisciplinary Optimization, 2023 - Springer
Uncertainty of quantitative models of input variables and computational model could
certainly cause the uncertainties of structural response and structural reliability. Hence …
certainly cause the uncertainties of structural response and structural reliability. Hence …
Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model
Due to inherent variability (ie, aleatory uncertainty) in material properties, loading conditions,
manufacturing processes, etc., simulation output responses should follow certain …
manufacturing processes, etc., simulation output responses should follow certain …