Title: Learning PDE Solution Operator for Continuous Modeling of Time-Series. Abstract: 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, the most prominent of which is Neural ODE. Most prior works make specific assumptions on the type of DEs or restrict them to first or second-order DEs, making the model specialized for certain problems. Furthermore, due to the use of numerical integration, they suffer from computational expensiveness and numerical instability. Building upon recent Fourier neural operator (FNO), this work proposes a partial differential equation (PDE) based framework which improves the dynamics modeling capability and circumvents the need for costly numerical integration. FNO is hard to be directly applied to real applications because it is mainly confined to physical PDE problems. To fill this void, we propose a continuous-in-time FNO to deal with irregularly-sampled time series and provide a theoretical result demonstrating its universality. Moreover, we reveal an intrinsic property of PDEs that increases the stability of the model. Several numerical evidence shows that our method represents a broader range of problems, including synthetic, image classification, and irregular time-series. Our framework opens up a new way for a continuous representation of neural networks that can be readily adopted for real-world applications.