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

Description

The Physics Informed Neural Networks (PINNs) course is a comprehensive training course that will prepare 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 and 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 using 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 don’t have any experience in machine learning or computational engineering, that’s not a problem. 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 have fun learning PINN together.

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

Numerical solution of the 1D heat equation using the finite difference method (FDM).
Numerical solution of the finite difference method (FDM) for the two-dimensional Berger equation.
A physics-informed neural network (PINN) solution for the 1D Berger 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.
Create the PDE solver numerically.
Create a pin-based PDE solver.
Understand the theory behind pin PDE solvers.