2 papers
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…
cs.LG2025
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…