What You Will Learn!
- Understand the Theory behind PDEs equations solvers.
- Build numerical based PDEs solver.
- Build PINNs based pdes solver.
- Understand the Theory behind PINNs PDEs solvers.
Description
Description
This is a complete course that will prepare you to use Physics-Informed Neural Networks (PINNs). We will cover the fundamentals of Solving partial differential equations (PDEs) and how to solve them using finite difference method as well as Physics-Informed Neural Networks (PINNs).
What skills will you Learn:
In this course, you will learn the following skills:
Understand the Math behind Finite Difference Method .
Write and build Algorithms from scratch to sole the Finite Difference Method.
Understand the Math behind partial differential equations (PDEs).
Write and build Machine Learning Algorithms to solve PINNs using Pytorch.
Write and build Machine Learning Algorithms to solve PINNs using DeepXDE.
Postprocess the results.
Use opensource libraries.
We will cover:
Finite Difference Method (FDM) Numerical Solution 1D Heat Equation.
Finite Difference Method (FDM) Numerical Solution for 2D Burgers Equation.
Physics-Informed Neural Networks (PINNs) Solution for 1D Burgers Equation.
Physics-Informed Neural Networks (PINNs) Solution for 2D Heat Equation.
Deepxde Solution for 1D Heat.
Deepxde Solution for 2D Navier Stokes.
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/ partial differential equations (PDEs) Physics-Informed Neural Networks (PINNs). Let's enjoy Learning PINNs together.
Who Should Attend!
- Engineers and Programmers whom want to Learn PINNs