Scalable gradient–enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes

MA Bouhlel, S He, JRRA Martins - Structural and Multidisciplinary …, 2020 - Springer
Airfoil shape design is one of the most fundamental elements in aircraft design. Existing
airfoil design tools require at least a few minutes to analyze a new shape and hours to …

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

PS Palar, LR Zuhal, K Shimoyama… - Reliability Engineering & …, 2018 - Elsevier
The presence of uncertainties is inevitable in engineering design and analysis, where failure
in understanding their effects might lead to the structural or functional failure of the systems …

Optimization of expensive black-box problems via Gradient-enhanced Kriging

L Chen, H Qiu, L Gao, C Jiang, Z Yang - Computer Methods in Applied …, 2020 - Elsevier
This paper explores the use of Gradient-enhanced Kriging for optimization of expensive
black-box design problems, which is not completely limited by the conventional Efficient …

[HTML][HTML] A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems

L Chen, H Qiu, L Gao, C Jiang, Z Yang - Applied Mathematical Modelling, 2019 - Elsevier
By exploring the auxiliary information from gradients, the accuracy of Kriging model can be
improved. However, the dramatically increased time for model training tends to be …

Gaussian process surrogate model with composite kernel learning for engineering design

P Satria Palar, L Rizki Zuhal, K Shimoyama - AIAA journal, 2020 - arc.aiaa.org
The composite kernel learning (CKL) method is introduced to efficiently construct composite
kernels for Gaussian process (GP) surrogate models with applications in engineering …

On the use of surrogate models in engineering design optimization and exploration: The key issues

PS Palar, RP Liem, LR Zuhal… - Proceedings of the genetic …, 2019 - dl.acm.org
Surrogate models are invaluable tools that greatly assist the process of computationally
expensive analyses and optimization. Engineering optimization reaps the benefit from …

Efficient uncertainty quantification for a hypersonic trailing-edge flap, using gradient-enhanced kriging

S Bhattrai, JHS de Baar, AJ Neely - Aerospace Science and Technology, 2018 - Elsevier
We present a numerical study on the uncertainty quantification (UQ) of aerodynamic forces
acting on a hypersonic trailing-edge flap model, as a result of input uncertainties in the …

Gaussian processes and support vector regression for uncertainty quantification in aerodynamics

PS Palar, K Zakaria, LR Zuhal, K Shimoyama… - AIAA Scitech 2021 …, 2021 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2021-0181. vid This paper investigates
the performance of two kernel-based surrogate models, namely Gaussian Process …

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

PS Palar, LR Zuhal, K Shimoyama - IEEE Access, 2023 - ieeexplore.ieee.org
Quantifying the impact of design variables in aerodynamic design exploration can provide
valuable insights to designers. Global sensitivity analysis (GSA) is a crucial tool in …

Polynomial-chaos–kriging with gradient information for surrogate modeling in aerodynamic design

LR Zuhal, K Zakaria, PS Palar, K Shimoyama, RP Liem - AIAA journal, 2021 - arc.aiaa.org
This paper proposes a variant of gradient-enhanced surrogate model based on polynomial-
chaos–kriging (PCK) to assist aerodynamic design exploration. The main aim is to improve …