This repository provides some basic examples of using deep neural networks and feed-forward and LSTM-like neural networks to solve ordinary differential equations (ODEs), partial differential ...

Partial Differential Equations (PDEs) are equations involving functions of multiple variables and their partial derivatives. They are essential in modeling various physical phenomena.

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster. In high ...