AI Technologies for Solving Ordinary Differential Equations

• Understand the Theory behind ODEs equations solvers.

• Build Analytical based ODEs solver.

• Build PINNs, Inv-PINNs, and DNO for ODE solver.

• Understand the Theory behind PINNs, Inv-PINNs, and DNO for ODEs solvers.

• High School Math

• Basic Python knowledge

This is a complete course that will prepare you to solve Ordinary Differential Equations (ODEs) using Physics-Informed Neural Networks (PINNs), Inverse Physics-Informed Neural Networks (Inv-PINNs), and Deep Neural Operator (DNO) We will cover the fundamentals of Solving Ordinary Differential Equations (ODEs) and how to prepare these equations for solving them using PINNs, Inv-PINNs, and DNO.

What skills will you Learn:

In this course, you will learn the following skills:

• Understand the General Math behind Ordinary Differential Equations (ODEs) .

• Write and build Algorithms from scratch to sole the Ordinary Differential Equations using Traditional methods.

• Understand the Math behind PINNs, Inv-PINNs, and DNO.

• Write and build Machine Learning Algorithms to solve PINNs, Inv-PINNs, and DNO using Pytorch.

• Postprocess the results.

• Compare the results acquired from traditional methods and PINNs related tech.

• Use opensource libraries.

We will cover:

• Pytoch Basics.

• Apply Solving ODEs Analytical solutions using Python.

• Solving ODEs Using PINNs for the Damped Harmonic Oscillator.

• Acquire an important related parameter Using Inverse-PINNs for the Damped Harmonic Oscillator.

• Predicting an output curve Using Deep Neural Operator (DNO).

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/ Ordinary Differential Equations (ODEs) / Physics-Informed Neural Networks (PINNs)/ Inverse Physics-Informed Neural Networks (Inv-PINNs)/ and Deep Neural Operator (DNO).

Let's enjoy Learning PINNs together...