## Description

Inverse Physics Informed Neural Networks (I-PINNs) course. This comprehensive course is designed to equip you with the skills to effectively use Physics Based Neural Networks (IPINN). We will cover the basic concepts of solving partial differential equations (PDEs) and show how to calculate simulation parameters through the use of inverse physics-based neural networks using data generated by solving PDEs with the finite difference method (FDM). we will give In this course, you will learn the following skills:

• Understand the mathematics behind the finite difference method.
• Write and build algorithms from scratch to exclusive with the finite difference method.
• Understand the mathematics behind partial differential equations (PDEs).
• Write and build machine learning algorithms to solve reverse pins using Pytorch.
• Write and build machine learning algorithms to solve inverted pins using DeepXDE.

We will cover:

• Pytorch matrix and the basics of tensors.
• Numerical solution of Finite Difference Method (FDM) for 1D Berger equation.
• A physics-informed neural network (PINN) solution for the 1D Berger’s equation.
• Total variation reduction (TVD) method solution for the 1D Berger’s equation.
• Inverse pins solution for the 1D Berger’s equation.
• Inverse pins for the two-dimensional Navier-Stokes equation using DeepXDE.

Don’t worry if you have no prior experience in machine learning or computational engineering. This course is comprehensive and periodic and provides a thorough understanding of machine learning and fundamental aspects of PDE and IPINN partial differential equations of neural networks with inverse physics information.

### What you will learn in Inverse Physics Informed Neural Networks (I-PINNs) course

• Understand the theory behind PDE equation solvers.

• Build the PDE solver numerically.

• Understand the theory behind inverse pin PDE solvers.

• Build an Inverse-PINN code solver.

### This course is suitable for people who

• Engineers and programmers who want to learn reverse pins

### Characteristics of Inverse Physics Informed Neural Networks (I-PINNs) course

• Publisher: Yudmi
• Training level: beginner to advanced
• Training duration: 7 hours and 48 minutes
• Number of courses: 41

### Inverse Physics Informed Neural Networks (I-PINNs) course prerequisites

• High School Math
• Basic Python knowledge

### Installation guide

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