PINNs Using Physics-Nemo [Modulus]

Description

This is a introductory course that will prepare you to work with Physics-Informed Neural Networks (PINNs) using The library of Modulus [Physics-Nemo]. We will cover the fundamentals of Solving partial differential equations (PDEs) using Physics-Informed Neural Networks (PINNs) from its basics and March towards solving PINNs with Modulus [Physics-Nemo].

What skills will you Learn:

In this course, you will learn the following skills:

• Understand the Math behind solving partial differential equations (PDEs) with PINNs.

• Write and build Machine Learning Algorithms to solve PINNs using Pytorch.

• Write and build Machine Learning Algorithms to solve PINNs using Modulus [Physics-Nemo].

• Postprocess the results.

• Use opensource libraries.

• Define your own PDEs to solve them or use built in equations (such as the N.S equations in Modulus [Physics-Nemo]).

  
  

We will cover:

• How to deploy Modulus [Physics-Nemo] on your own computer GPU and in Google Collab.

• Physics-Informed Neural Networks (PINNs) Solution for 1D Burgers Equation using pytorch.

• Physics-Informed Neural Networks (PINNs) Solution for  1D wave Equation using Modulus [Physics-Nemo].

• Physics-Informed Neural Networks (PINNs) Solution for  cavity flow problem using Modulus [Physics-Nemo].

• Physics-Informed Neural Networks (PINNs) Solution for  2D heat sink flow problem using Modulus [Physics-Nemo].

If you do not have prior experience in Machine Learning or Computational Engineering, that's no problem. This course is complete and concise, covering the fundamentals of Machine Learning/ Physics-Informed Neural Networks (PINNs). Let's enjoy Learning Modulus [Physics-Nemo] together.