3 papers
cs.LG2026
Physics and Data Driven Transformer-Mamba Framework for Flow Field
Zhuo Zhang, Shun Zou, Canqun Yang +1
While deep learning accelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with gene…
cs.LG2025
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…
stat.ML2025
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…