Download Udemy – Physics Informed Neural Networks (PINNs) 2023-9

Description

The Physics Informed Neural Networks (PINNs) course is a complete training course that prepares you to use Physics Informed Neural Networks (PINNs). We will cover the principles of solving partial differential equations (PDEs) and how to solve them using the finite difference method as well as physics-based neural networks (PINN).

In this course, you will learn the following skills:

Understand the mathematics behind the finite difference method.
Write and build algorithms from scratch to uniqueness with the finite difference method.
Understand the mathematics behind partial differential equations (PDEs).
Write and build machine learning algorithms to solve pins using Pytorch.
Write and build machine learning algorithms to solve pins using DeepXDE.
Results after processing
Use open source libraries.

If you have no previous experience in machine learning or computational engineering, that’s okay. This course is comprehensive and concise and covers the fundamentals of Machine Learning/Partial Differential Equations (PDEs) of Physics-Based Neural Networks (PINN). Let’s enjoy learning Pins together.

What you will learn in Physics Informed Neural Networks (PINNs) course

Finite difference method (FDM) numerical solution of 1D thermal equation.
Numerical solution of Finite Difference Method (FDM) for two-dimensional Berger’s equation.
A physics-informed neural network (PINN) solution for the 1D Berger’s equation.
A physics-informed neural network (PINN) solution for the two-dimensional heat equation.
Deepxde solution for 1D heat.
Deepxde solution for 2D Navier Stocks.
Understand the theory behind PDE equation solvers.
Build the PDE solver numerically.
Build a pin-based pdes solver.
Understand the theory behind pin PDE solvers.