Differential equations are widely used in any scientific field. A new method of solving differential equations using deep learning methods has great prospects and have greater versatility over the grid numerical methods. Neural networks can be used as a method for efficiently solving difficult partial differential equations(PDEs). The library that provides the method for solving a general form of PDEs using deep learning will be a useful tool for many scientists and engineers, as well as students.

This project aims to design of a general solver for different types of PDEs using deep learning approach base on the PINNs algorithm as part of NeuralPDE.jl library using the ModelingToolkit.jl PDE interface.



Kirill Zubov


  • Vaibhav Dixit
  • Lu Lu
  • ChrisRackauckas