SineNet: Learning Temporal Dynamics in Time-Dependent Partial
Differential Equations
SineNet: Learning Temporal Dynamics in Time-Dependent Partial
Differential Equations
We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dynamics. While the U-Net architecture with skip connections is commonly used by prior studies to enable multi-scale processing, our analysis shows that the need for features to evolve …