AI Technologies for Solving Ordinary Differential Equations
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Section 1 : Introduction

lecture 1: Introduction
lecture 2: Installing Anaconda
lecture 3: Install PyTorch CUDA
Section 2: Pytorch Basics

lecture 4: Deep Learning Theory
lecture 5: PyTorch Tensors Basics
lecture 6: Tensors to NumPy arrays
lecture 7: Backpropagation Theory
lecture 8: Backpropagation using PyTorch
Section 3: Solving ODE Using PINNs

Lecture 9: How PINNs Works
Lecture 10: Damped Harmonic Oscillator Exact Solution
Lecture 11: Coding the Exact Solution
Lecture 12: Define ODEs Deep Neural Network
Lecture 13: Set the Training Process
Lecture 14: Define the DNN Loss
Lecture 15: Results Evaluation
Lecture 16: Change the Loss Function
Lecture 17: Change the Network Size
Lecture 18: Full Code Summary
CODE
Section 4: Inverse Physics Informed Neural Networks (I-PINNs)

Lecture 19: Lets start with Inverse PINNs ODE
Lecture 20: Data Creation For the Inverse PINNs
Lecture 21: Define the IPINNS Network
Lecture 22: Set The Training Process
Lecture 23: Run the Training
Lecture 24: Results Review
Code
Section 5: Solving ODE Using Deep Neural Operator (DNO)

Lecture 25: Integration Data Creation
Lecture 26: Data Preprocessing Part 1
Lecture 27: Data Preprocessing Part 2
Lecture 28: Model Build Up
Lecture 29: Set The Training Process
Lecture 30: Results Review
CODE

lecture 7: Backpropagation Theory

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lecture 7:

Backpropagation Theory