Results 201 to 210 of about 113,545 (266)
Some of the next articles are maybe not open access.
A Fast Kriging-Assisted Evolutionary Algorithm Based on Incremental Learning
IEEE Transactions on Evolutionary Computation, 2021Kriging models, also known as Gaussian process models, are widely used in surrogate-assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the standard Kriging models limits their usage in high-dimensional optimization. To tackle
Dawei Zhan, Huanlai Xing
semanticscholar +1 more source
AK-DS: An adaptive Kriging-based directional sampling method for reliability analysis
Mechanical systems and signal processing, 2021In this paper, a novel reliability method called AK-DS for adaptive Kriging (AK)-based directional sampling (DS) is proposed to efficiently estimate small failure probability.
Xiaobo Zhang, Zhenzhou Lu, Kai Cheng
semanticscholar +1 more source
Empirical Bayesian kriging implementation and usage.
Science of the Total Environment, 2020We described the key features of the pragmatic geostatistical methodology aiming at resolving the following drawbacks of classical geostatistical models: assuming that the data is the realization of a stationary process; assuming that the data values are
A. Gribov, K. Krivoruchko
semanticscholar +1 more source
Bioresource Technology, 2021
Kernel extreme learning machine (KELM) and Kriging models are proposed to predict biochar adsorption efficiency of heavy metals. Both six popular ions (Pb2+, Cd2+, Zn2+, Cu2+, Ni2+, As3+) and single ion are considered to test the accuracy of KELM and ...
Ying Zhao +4 more
semanticscholar +1 more source
Kernel extreme learning machine (KELM) and Kriging models are proposed to predict biochar adsorption efficiency of heavy metals. Both six popular ions (Pb2+, Cd2+, Zn2+, Cu2+, Ni2+, As3+) and single ion are considered to test the accuracy of KELM and ...
Ying Zhao +4 more
semanticscholar +1 more source
Reliability Engineering & System Safety, 2020
This paper proposes a system active learning Kriging (SALK) method to handle system reliability-based design optimization (SRBDO) problems, where responses of all constraints at an input can be obtained simultaneously by running a multiple response model.
Mi Xiao, Jinhao Zhang, Liang Gao
semanticscholar +1 more source
This paper proposes a system active learning Kriging (SALK) method to handle system reliability-based design optimization (SRBDO) problems, where responses of all constraints at an input can be obtained simultaneously by running a multiple response model.
Mi Xiao, Jinhao Zhang, Liang Gao
semanticscholar +1 more source
, 2020
A major issue in the structural reliability analysis is to determine an accurate estimation result of the failure probability ideally based on a small number of model evaluations.
Xufang Zhang, Lei Wang, J. Sørensen
semanticscholar +1 more source
A major issue in the structural reliability analysis is to determine an accurate estimation result of the failure probability ideally based on a small number of model evaluations.
Xufang Zhang, Lei Wang, J. Sørensen
semanticscholar +1 more source
Reliability and sensitivity analysis of composite structures by an adaptive Kriging based approach
Composite structures, 2021Uncertainties widely existing in composite structures make the reliability and sensitivity analysis highly necessary. In this work, the approach based on adaptive Kriging is discussed to estimate the failure probability, local and global sensitivity of ...
Changcong Zhou +4 more
semanticscholar +1 more source
Engineering Failure Analysis, 2021
Mechanical system is usually composed of multiple complex structures, which endure the combine action of multi-physical fields (e.g., flow field, thermal field, structural field, and so forth) during operation.
Cheng-rong Lu +5 more
semanticscholar +1 more source
Mechanical system is usually composed of multiple complex structures, which endure the combine action of multi-physical fields (e.g., flow field, thermal field, structural field, and so forth) during operation.
Cheng-rong Lu +5 more
semanticscholar +1 more source
Evaluation of empirical Bayesian kriging
Spatial Statistics, 2019Using Bayesian bootstrap as the informative prior distribution is a key feature of the empirical Bayesian kriging method. We evaluate correctness of the method using two large simulation experiments. We also compare empirical Bayesian kriging performance
K. Krivoruchko, A. Gribov
semanticscholar +1 more source