3 citations · 3 across the 6 of their papers we have counts for
6 papers
IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry
Ying Yan, Liwei Hu, Xiaoming Zhang
Existing generative models learn data distributions in flat Euclidean space. However, most data in our real world are manifolds embedded in high dimensional Euclidean space. Theref…
Geodesic Gradient Descent: A Generic and Learning-rate-free Optimizer on Objective Function-induced Manifolds
Liwei Hu, Guangyao Li, Wenyong Wang +2
Euclidean gradient descent algorithms barely capture the geometry of objective function-induced hypersurfaces and risk driving update trajectories off the hypersurfaces. Riemannian…
Learning with Geometry: Including Riemannian Geometric Features in Coefficient of Pressure Prediction on Aircraft Wings
Liwei Hu, Wenyong Wang, Yu Xiang +1
We propose to incorporate Riemannian geometric features from the geometry of aircraft wing surfaces in the prediction of coefficient of pressure (CP) on the aircraft wing. Contrary…
A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method
Yu Xiang, Guangbo Zhang, Liwei Hu +2
Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction. The obtained airfoils geometrical fea…
Aerodynamic Data Predictions Based on Multi-task Learning
Liwei Hu, Yu Xiang, Jun Zhan +2
The quality of datasets is one of the key factors that affect the accuracy of aerodynamic data models. For example, in the uniformly sampled Burgers' dataset, the insufficient high…
Flow Field Reconstructions with GANs based on Radial Basis Functions
Liwei Hu, Wenyong Wang, Yu Xiang +1
Nonlinear sparse data regression and generation have been a long-term challenge, to cite the flow field reconstruction as a typical example. The huge computational cost of computat…