The TorchPhysics Tutorial

Here one can find all important information and knowledge to get started with the software library TorchPhysics.

In order to make it as easy and user-friendly as possible for all users, both complete novices and professionals in Machine Learning and PDEs, to get started, the tutorial starts with some basics regarding differential equations and neural networks. For more experienced users, these points can be skipped.

Afterward, we give a rough overview of different Deep Learning approaches for solving differential equations, with a focus on PINNs and DeepONet.

The main and final topic is the use of TorchPhysics. Here we split the tutorial into two parts. The first part, guides you along some implementations of different small examples, while showing all the important aspects and steps to solve a differential equation in TorchPhysics. This tutorial series is aimed at an audience which is more interested on the direct utilization of the library and for getting a fast and small overview of the possibilities.

To get a deeper understanding of the library, we show in the second part how the library is internally structured. This series is more aimed for users who plan to add or change functionalities.

Basics tutorial

• Physics Informed Neural Networks (PINNs) in TorchPhysics

Applied tutorial

• Applied TorchPhysics Tutorials

In depth tutorial

• Tutorial: Understanding the structure of TorchPhysics