Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems
The equilibrium optimizer (EO) is a recently proposed physics-based metaheuristic
algorithm inspired by the dynamic mass balance on a control volume. However, EO may …
algorithm inspired by the dynamic mass balance on a control volume. However, EO may …
Analytical robust design optimization based on a hybrid surrogate model by combining polynomial chaos expansion and Gaussian kernel
Robust design optimization (RDO) is one of the most popular methodologies in the presence
of uncertainties, which aims to provide an insensitive design configuration. However, the …
of uncertainties, which aims to provide an insensitive design configuration. However, the …
Investigation on uncertainty quantification of transonic airfoil using compressive sensing greedy reconstruction algorithms
H Handuo, S Yan**, Y Jianyang, L Yao… - Aerospace Science and …, 2024 - Elsevier
Uncertainty quantification constructs the stochastic responses of system output under
uncertainties. Traditional uncertainty quantification methods such as Monte Carlo and Full …
uncertainties. Traditional uncertainty quantification methods such as Monte Carlo and Full …
An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and …
Polynomial dimensional decomposition (PDD) is a surrogate method originated from the
ANOVA (analysis of variance) decomposition, and has shown powerful performance in …
ANOVA (analysis of variance) decomposition, and has shown powerful performance in …
A new polynomial chaos expansion method for uncertainty analysis with aleatory and epistemic uncertainties
W He, C Gao, G Li, J Zhou - Structural and Multidisciplinary Optimization, 2024 - Springer
The probability and evidence theories are frequently used tool to deal with the mixture of
aleatory and epistemic uncertainties. Due to the double-loop procedure for the mixed …
aleatory and epistemic uncertainties. Due to the double-loop procedure for the mixed …
Global sensitivity analysis with limited data via sparsity-promoting D-MORPH regression: Application to char combustion
In uncertainty quantification, variance-based global sensitivity analysis quantitatively
determines the effect of each input random variable on the output by partitioning the total …
determines the effect of each input random variable on the output by partitioning the total …
High-Performance Krawtchouk Polynomials of High Order Based on Multithreading
Orthogonal polynomials and their moments serve as pivotal elements across various fields.
Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal …
Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal …
Global sensitivity analysis in the limited data setting with application to char combustion
In uncertainty quantification, variance-based global sensitivity analysis quantitatively
determines the effect of each input random variable on the output by partitioning the total …
determines the effect of each input random variable on the output by partitioning the total …