Neural-PDE: A RNN based neural network for solving time dependent PDEs

TL;DR

Neural-PDE is a deep learning framework using bidirectional LSTM to learn and simulate time-dependent PDEs from data, effectively capturing complex dynamics with reduced computational cost.

Contribution

It introduces a novel RNN-based neural network that learns governing rules of PDEs directly from data, capable of handling multiscale variables and high-dimensional systems.

Findings

Successfully learns PDE dynamics within 20 epochs

Accurately predicts multiple time steps of PDE solutions

Reduces computational complexity compared to traditional methods

Abstract

Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering. Numerically solving nonlinear and/or high-dimensional PDEs is often a challenging task. Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence deep learning framework called Neural-PDE, which allows to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the next n time steps data. One critical feature of our proposed framework is that the Neural-PDE is able to simultaneously learn and simulate the multiscale variables.We test the Neural-PDE by a range of examples from one-dimensional PDEs to a high-dimensional and nonlinear complex fluids model. The results show that the Neural-PDE is capable of learning the initial conditions, boundary conditions and differential operators without the knowledge of the specific form of a PDE system.In our experiments the Neural-PDE can efficiently extract the dynamics within 20 epochs training, and produces accurate predictions. Furthermore, unlike the traditional machine learning approaches in learning PDE such as CNN and MLP which require vast parameters for model precision, Neural-PDE shares parameters across all time steps, thus considerably reduces the computational complexity and leads to a fast learning algorithm.