6 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…
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…
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…
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…
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…
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…