16 citations · 53 across the 8 of their papers we have counts for
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physics.flu-dyn2026★ 16 cited
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…
physics.flu-dyn2026★ 1 cited
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.flu-dyn2026
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…