PINNs Using NVIDIA Modulus
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Section 1: Introduction

lecture 1: Introduction
lecture 2: Course Structure
lecture 3: Deep Learning Theory
lecture 4: PINNs Theory
ref.txt
Section2: PINNs Solution for 1D Burgers Equation with Pytorch

Section-2 Files
lecture 5: Define the Neural Network
lecture 6: Initial Conditions and Boundary Conditions
lecture 7: Optimizer
lecture 8: Loss Function
lecture 9: Train the Model
lecture 10: Results Evaluation
Section 3: 1D Wave Equation

Section-3 Files
lecture 11: What is the Wave Equation
lecture 12: Setting Up Google Colab
lecture 13: Define the Wave Equation Function
lecture 14: Define the Config File
lecture 15: Import Needed Libraries
lecture 16: Set Up the main RUN File
lecture 17: Define the B.C, Interior Points
lecture 18: Add Validator Functionality
lecture 19: Solve
lecture 20: Results Extraction
lecture 21: Results Post Processing
Section 4: Cavity Flow

Section-4 Files
lecture 22: Setting up env. in your personal computer
lecture 23: Cavity Flow Problem
lecture 24: Define the Config File
lecture 25: Import Needed Libraries
lecture 26: Set Up the main RUN File
lecture 27: Define the Navier-Stokes equation and DNN
lecture 28: Define the B.C, I.C, Interior Points
lecture 29: Solve
lecture 30: Results Extraction
lecture 31: Results Post Processing
lecture 32: Pretrained Model Inference
Section 5: 2D Heat Sink

Section-5 Files
lecture 33: 2D heat channel problem
lecture 34: Define the Config File
lecture 35: Import Needed Libraries
lecture 36: Set Up the main RUN File
lecture 37: Define the geometry
lecture 38: Define the Navier-Stokes equation and DNN
lecture 39: Define the B.C, Interior Constraints
lecture 40: Add Monitor
lecture 41: Solve
lecture 42: Results Extraction
lecture 43: Results Post Processing
Section 6: 2D Stress Analysis

Section-6 Files
lecture 44: 2D Stress Analysis Problem
lecture 45: Define the Config File
lecture 46: Import Needed Libraries
lecture 47: Define the DNN
lecture 48: Define the geometry - part a
lecture 49: Define the geometry - part b
lecture 50: Set Changing Parameters
lecture 51: Define the B.C, Interior Constraints
lecture 52: Case Inferencing
lecture 53: Solve
lecture 54: Results Post Processing

lecture 9: Train the Model

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