Learning PDE Solution Operator for Continuous Modeling of Time-Series

Understand

Learning underlying dynamics from data is important and challenging in many real-world scenarios.

• Incorporating differential equations (DEs) to design continuous networks has drawn much attention recently, however, most prior works make specific assumptions on the type of DEs, making the model specialized for particular problems.
• This work presents a partial differential equation (PDE) based framework which improves the dynamics modeling capability.
• Building upon the recent Fourier neural operator, we propose a neural operator that can handle time continuously without requiring iterative operations or specific grids of temporal discretization.