lecture 35: Define the Network and the PDE

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Physics Informed Neural Networks (PINNs)

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Section 1: Introduction

lecture 1: Introduction

lecture 2: Installing Anaconda

lecture 3: Course Structure1

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 Function4

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