3 citations · 6 across the 12 of their papers we have counts for
12 papers
LGFNet: Local-Global Fusion Network with Fidelity Gap Delta Learning for Multi-Source Aerodynamics
Qinye Zhu, Yu Xiang, Jun Zhang +1
The precise fusion of computational fluid dynamic (CFD) data, wind tunnel tests data, and flight tests data in aerodynamic area is essential for obtaining comprehensive knowledge o…
Disentangling Granularity: An Implicit Inductive Bias in Factorized VAEs
Zihao Chen, Yu Xiang, Wenyong Wang
Despite the success in learning semantically meaningful, unsupervised disentangled representations, variational autoencoders (VAEs) and their variants face a fundamental theoretica…
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation
Zihao Chen, Wenyong Wang, Jiachen Yang +1
Geometric representation learning in preserving the intrinsic geometric and topological properties for discrete non-Euclidean data is crucial in scientific applications. Previous r…
Isometric Immersion Learning with Riemannian Geometry
Zihao Chen, Wenyong Wang, Yu Xiang
Manifold learning has been proven to be an effective method for capturing the implicitly intrinsic structure of non-Euclidean data, in which one of the primary challenges is how to…
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…
Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures
Haoming Li, Yu Xiang, Haodong Xu +1
As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data. In this paper, w…
Novel deep learning methods for 3D flow field segmentation and classification
Xiaorui Bai, Wenyong Wang, Jun Zhang +2
Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods mainly based on global informatio…
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…
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
Yu Xiang, Yu Huang, Haodong Xu +2
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model…
A Vehicles Control Model to Alleviate Traffic Instability
Jiancheng Fang, Yu Xiang, Yu Huang +2
While bringing convenience to people, the growing number of vehicles on road already cause inevitable traffic congestion. Some traffic congestion happen with observable reasons, bu…
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…