Inverse Physics Informed Neural Networks (I-PINNs)
- Buy now
- Learn more
Section 1: Introduction

lecture 1: Introduction
lecture 2: Course structure
lecture 3: Install PyTorch / CUDA
links.txt
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 for 1D Burgers Equation

Section-3 Files
lecture 9: Pre-processing
lecture 10: Solving the Equation
lecture 11: Post-processing
lecture 12: Solver Failure!
Section 4: PINNs Solution for 1D Burgers Equation

Section-4 Files
lecture 13: PINNs Theory
lecture 14: Define the Neural Network
lecture 15: Initial Conditions and Boundary Conditions
lecture 16: Optimizer
lecture 17: Loss Function
lecture 18: Train the Model
lecture 19: Results Evaluation
Section 5: TVD Method Solution for 1D Burgers Equation

Section-5 Files
lecture 20: Pre-processing
lecture 21: Solving the Equation
lecture 22: Post-processing
lecture 23: PINNs VS TVD , Results Comparison
Section 6: PyTorch: Inverse-PINNs (IPINNs) Solution for 1D Burgers Equation

Section-6 Files
lecture 24: Inverse-PINNs Theory
lecture 25: Create The Training Data
lecture 26: Define the Neural Network
lecture 27: Domain Data Input
lecture 28: Optimizer
lecture 29: Loss Function
lecture 30: Train the Model
lecture 31: PINNs VS TVD VS IPINNs , Results Comparison
Section 7: DeepXDE: Inverse-PINNs for Navier Stokes Equation

Section-7 Files
lecture 32: Inverse-PINNs Problem Setting
lecture 33: Create The Training Data -Part 1
lecture 34: Create The Training Data -Part 2
lecture 35: Define the Neural Network
lecture 36: Initial Conditions and Boundary Conditions
lecture 37: Loss Function
lecture 38: Optimizer
lecture 39: Train the Model
lecture 40: Results Evaluation

lecture 24: Inverse-PINNs Theory

lecture 24:

Inverse-PINNs Theory