Physics Informed Neural Networks (PINNs)
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Section 1: Introduction

lecture 1: Introduction
lecture 2: Installing Anaconda
lecture 3: Course Structure
ref.txt
Section 2: Pytorch Basics

Section-2 Files
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: FDM Numerical Solution 1D Heat Equation

Section-3 Files
lecture 9: Numerical solution theory
lecture 10: Pre-processing
lecture 11: Solving the Equation
lecture 12: Post-processing
Section 4: FDM Numerical Solution for 2D Burgers Equation

Section-4 Files
lecture 13: Pre-processing
lecture 14: Solving the Equation
lecture 15: Post-processing
Section 5: PINNs Solution for 1D Burgers Equation

Section-5 Files
lecture 16: PINNs Theory
lecture 17: Define the Neural Network
lecture 18: Initial Conditions and Boundary Conditions
lecture 19: Optimizer
lecture 20: Loss Function
lecture 21: Train the Model
lecture 22: Results Evaluation
Section 6: PINNs Solution for 2D Heat Equation

Section-6 Files
lecture 23: Define the Neural Network
lecture 24: Initial Conditions and Boundary Conditions
lecture 25: Optimizer
lecture 26: Loss Function
lecture 27: Train the Model
lecture 28: Results Evaluation
Section 7: DeepXDE Solution for 1D Heat

Section-7 Files
lecture 29: Set Geometry, B.C and I.C
lecture 30: Define the Network and the PDE
lecture 31: Train the model
lecture 32: Result evaluation
Section 8: DeepXDE Solution for 2D Navier Stokes

Section-8 Files
lecture 33: Set Geometry
lecture 34: Set Boundary Conditions
lecture 35: Define the Network and the PDE
lecture 36: Train the model
lecture 37: Result evaluation

lecture 18: Initial Conditions and Boundary Conditions

lecture 18:

Initial Conditions and Boundary Conditions