# PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

## TL;DR

PDE-Net 2.0 is a hybrid deep learning model that learns PDEs directly from data, combining numerical differential operator approximation with symbolic neural networks, enabling discovery and long-term prediction of complex dynamical systems.

## Contribution

It introduces PDE-Net 2.0, a flexible neural network that learns both differential operators and nonlinear responses for data-driven PDE discovery.

## Key findings

- Successfully uncovers hidden PDEs from observed data.
- Predicts dynamical behavior accurately over extended periods.
- Operates effectively even with noisy data.

## Abstract

Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data-driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE-Net 2.0, to discover (time-dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE-Net 2.0 is based on our earlier work \cite{Long2018PDE} where the original version of PDE-Net was proposed. PDE-Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi-layer neural network for model recovery. Comparing with existing approaches, PDE-Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE-Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.