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On efficient global optimization via universal Kriging surrogate models

Structural and multidisciplinary optimization, 2018-06, Vol.57 (6), p.2377-2397

2018

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Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations

Reliability engineering & system safety, 2023-04, Vol.232, p.109045, Article 109045

2023

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Efficient global optimization with ensemble and selection of kernel functions for engineering design

Structural and multidisciplinary optimization, 2019, Vol.59 (1), p.93-116

2019

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Improving ionic conductivity of doped Li7La3Zr2O12 using optimized machine learning with simplistic descriptors

Materials letters, 2022-02, Vol.308, p.131159, Article 131159

2022

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A comparative study of multi-objective expected improvement for aerodynamic design

Aerospace science and technology, 2019-08, Vol.91, p.548-560

2019

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Discovering chiral auxetic structures with near-zero Poisson's ratio using an active learning strategy

Materials & design, 2024-08, Vol.244, p.113133, Article 113133

2024

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On kernel functions for bi-fidelity Gaussian process regressions

Structural and multidisciplinary optimization, 2023-02, Vol.66 (2), p.37, Article 37

2023

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Design exploration of additively manufactured chiral auxetic structure using explainable machine learning

Materials & design, 2023-08, Vol.232, p.112128, Article 112128

2023

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A comparative study of local search within a surrogate-assisted multi-objective memetic algorithm framework for expensive problems

Applied soft computing, 2016-06, Vol.43, p.1-19

2016

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Multiobjective design optimization of stent geometry with wall deformation for triangular and rectangular struts

Medical & biological engineering & computing, 2019, Vol.57 (1), p.15-26

2019

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Multi-objective design space exploration using explainable surrogate models

Structural and multidisciplinary optimization, 2024-03, Vol.67 (3), p.38, Article 38

2024

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Global Sensitivity Analysis in Aerodynamic Design using Shapley Effects and Polynomial Chaos Regression

IEEE access, 2023-01, Vol.11, p.1-1

2023

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Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia

Buildings (Basel), 2023-01, Vol.13 (1), p.59

2023

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Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Reliability engineering & system safety, 2018-02, Vol.170, p.175-190

2018

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Response Surface based Analysis to Improve the Intravascular Stent Design Performance on Optimizing Wall Shear Stress Conditions

Transactions of Japanese Society for Medical and Biological Engineering, 2017, Vol.55Annual(5AM-Abstract), pp.415-415

2017

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Multi-fidelity non-intrusive polynomial chaos based on regression

Computer methods in applied mechanics and engineering, 2016-06, Vol.305, p.579-606

2016

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On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning

Reliability engineering & system safety, 2021-11, Vol.215, p.107848, Article 107848

2021

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Performance assessment of Kriging with partial least squares for high-dimensional uncertainty and sensitivity analysis

Structural and multidisciplinary optimization, 2023-05, Vol.66 (5), p.115, Article 115

2023

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Global sensitivity analysis of stochastic re-entry trajectory using explainable surrogate models

Acta astronautica, 2024-09, Vol.222, p.109-125

2024

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Maximizing the thrust performance of flexible caudal fin panels via experimental optimization

Ocean engineering, 2022-12, Vol.266, p.112969, Article 112969

2022

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A multi-point mechanism of expected hypervolume improvement for parallel multi-objective bayesian global optimization

Proceedings of the Genetic and Evolutionary Computation Conference, 2019, p.656-663

2019

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Corrigendum to “Multi-Fidelity Non-Intrusive Polynomial Chaos Based on Regression” [Comput. Methods Appl. Mech. Engrg. 305 (2016) 579–606]

Computer methods in applied mechanics and engineering, 2016-08, Vol.307, p.489-490

2016

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Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

Machine Learning, Optimization, and Data Science, 2020, Vol.11943, p.579-591

2020

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Ensemble of Kriging with Multiple Kernel Functions for Engineering Design Optimization

Bioinspired Optimization Methods and Their Applications, 2018, Vol.10835, p.211-222

2018

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Comparison of scalarization functions within a local surrogate assisted multi-objective memetic algorithm framework for expensive problems

2015 IEEE Congress on Evolutionary Computation (CEC), 2015, p.862-869

2015

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On efficient global optimization via universal Kriging surrogate models

Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations

Efficient global optimization with ensemble and selection of kernel functions for engineering design

Improving ionic conductivity of doped Li7La3Zr2O12 using optimized machine learning with simplistic descriptors

A comparative study of multi-objective expected improvement for aerodynamic design

Discovering chiral auxetic structures with near-zero Poisson's ratio using an active learning strategy

On kernel functions for bi-fidelity Gaussian process regressions

Design exploration of additively manufactured chiral auxetic structure using explainable machine learning

A comparative study of local search within a surrogate-assisted multi-objective memetic algorithm framework for expensive problems

Multiobjective design optimization of stent geometry with wall deformation for triangular and rectangular struts

Multi-objective design space exploration using explainable surrogate models

Global Sensitivity Analysis in Aerodynamic Design using Shapley Effects and Polynomial Chaos Regression

Gaussian Process Regression for Seismic Fragility Assessment: Application to Non-Engineered Residential Buildings in Indonesia

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Response Surface based Analysis to Improve the Intravascular Stent Design Performance on Optimizing Wall Shear Stress Conditions

Multi-fidelity non-intrusive polynomial chaos based on regression

On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning

Performance assessment of Kriging with partial least squares for high-dimensional uncertainty and sensitivity analysis

Global sensitivity analysis of stochastic re-entry trajectory using explainable surrogate models

Maximizing the thrust performance of flexible caudal fin panels via experimental optimization

A multi-point mechanism of expected hypervolume improvement for parallel multi-objective bayesian global optimization

Corrigendum to “Multi-Fidelity Non-Intrusive Polynomial Chaos Based on Regression” [Comput. Methods Appl. Mech. Engrg. 305 (2016) 579–606]

Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

Ensemble of Kriging with Multiple Kernel Functions for Engineering Design Optimization

Comparison of scalarization functions within a local surrogate assisted multi-objective memetic algorithm framework for expensive problems

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