What you'll learn:
- Build PINNs based pdes solver.
- Understand the Theory behind PINNs PDEs solvers.
- Build models using The library of Modulus [Physics-Nemo]
- Deploy The library of Modulus [Physics-Nemo] useing GoogleColab and your own GPU
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.
