16 citations · 53 across the 8 of their papers we have counts for
8 papers
KT-EGO: A Knowledge Transfer Assisted Efficient Global Optimization Algorithm for Solving High-Dimensional Expensive Black-Box Problems
Qineng Wang, Liming Song, Yun Chen +3
Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, we propose a knowledge transfer assisted…
Parallel Multi-Fidelity Expected Improvement Method for Efficient Global Optimization
Zhendong Guo, Qineng Wang, Liming Song +1
Multi-Fidelity optimization (MFO) has received extensive attentions in engineering design, which resorts to augmenting the small number of expensive high-fidelity (HF) samples by a…
A Dynamic Aggregation Strategy Enhanced Efficient Global Optimization Algorithm for Solving High-Dimensional Turbomachinery Design Problems
Qineng Wang, Zhendong Guo, Yun Chen +3
In order to solve the high-dimensional () expensive black-box problems within budget, an efficient global optimization (EGO) algorithm with a dynamic aggregation strateg…
A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operator
Qineng Wang, Zhendong Guo, Liming Song +1
To enable flexible and rapid aerodynamic performance evaluation in turbomachinery design, this paper proposes a panoramic performance prediction framework. Unlike most previous pre…
Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators
Qineng Wang, Liming Song, Tianyuan Liu +1
In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turbine endwall with variable nu…
A Novel Multi-fidelity Surrogate for Efficient Turbine Design Optimization
Qineng Wang, Liming Song, Zhendong Guo +2
To solve the turbine design optimization problems efficiently, surrogate-based optimization (SBO) algorithms are frequently used. To further reduce the cost of turbine design, the…
A Novel Multi-fidelity Surrogate for Turbomachinery Design Optimization
Qineng Wang, Liming Song, Zhendong Guo +2
Turbomachinery design optimization involves expensive black-box problems. Sample-efficient multi-fidelity optimization (MFO) offers an efficient solution. By utilizing multi-fideli…
Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
Biao Chen, Jing Wang, Hairun Xie +4
Neural operators have emerged as powerful deep learning frameworks for approximating solution operators of parameterized partial differential equations (PDE). However, current meth…