lecture 28: Model Build Up

lecture 28: Model Build Up

lecture 28: Model Build Up

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lecture 28:

Model Build Up

Simulation By Deep Neural Operator (DeepONets)

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Section 1: Introduction

lecture 1: Introduction

lecture 2: Install PyTorch / CUDA

lecture 3: Course structure

lecture 4: Deep Neural Operator

Ref.txt

Section 2: Pytorch Basics

Section-2 Files

lecture 5: Deep Learning Theory

lecture 6: PyTorch Tensors Basics

lecture 7: Tensors to NumPy arrays

lecture 8: Backpropagation Theory

lecture 9: Backpropagation using PyTorch

Section 3: FDM Numerical Solution 1D Heat Equation

Section-3 Files

lecture 10: Numerical solution theory

lecture 11: Pre-processing

lecture 12: Solving the Equation

lecture 13: Post-processing

Section 4: ODE Integration Neural Operator using PyTorch

Section-4 Files

lecture 14: Data creation

lecture 15: Data Preprocessing - Part 1

lecture 16: Data Preprocessing - Part 2

lecture 17: Model Build Up

lecture 18: Training Process

lecture 19: Results Evaluation6

Section 5: 1D Heat Equation Neural Operator using PyTorch

Section-5 Files

lecture 20: Data creation

lecture 21: Data Preprocessing - Part 1

lecture 22: Data Preprocessing - Part 2

lecture 23: Model Build Up

lecture 24: Training Process

lecture 25: Results Evaluation

Section 6: 1D Heat Equation Neural Operator using DeepXDE

Section-6 Files

lecture 26: Data creation

lecture 27: Data Preprocessing

lecture 28: Model Build Up

lecture 29: Training Process

lecture 30: Results Evaluation

Section 7: 2D Fluid Neural Operator using DeepXDE

Section-7 Files

lecture 31: Data creation3

lecture 32: Data Preprocessing

lecture 33: Model Build Up

lecture 34: Training Process

lecture 35: Results Evaluation4