1 citations · 2 across the 5 of their papers we have counts for
5 papers
PGOT: A Physics-Geometry Operator Transformer for Complex PDEs
Zhuo Zhang, Xi Yang, Ying Miao +5
While Transformers have demonstrated remarkable potential in modeling Partial Differential Equations (PDEs), modeling large-scale unstructured meshes with complex geometries remain…
Physics-Informed Neural Networks and Neural Operators for Parametric PDEs
Zhuo Zhang, Xiong Xiong, Sen Zhang +2
PDEs arise ubiquitously in science and engineering, where solutions depend on parameters (physical properties, boundary conditions, geometry). Traditional numerical methods require…
Separated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs
Xiong Xiong, Zhuo Zhang, Rongchun Hu +2
Solving high-frequency oscillatory partial differential equations (PDEs) is a critical challenge in scientific computing, with applications in fluid mechanics, quantum mechanics, a…
OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
Shun Zou, Zhuo Zhang, Guangwei Gao
Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glau…
MambaMIC: An Efficient Baseline for Microscopic Image Classification with State Space Models
Shun Zou, Zhuo Zhang, Yi Zou +1
In recent years, CNN and Transformer-based methods have made significant progress in Microscopic Image Classification (MIC). However, existing approaches still face the dilemma bet…