lecture 32: Inverse-PINNs Problem Setting

lecture 32: Inverse-PINNs Problem Setting

lecture 32: Inverse-PINNs Problem Setting

.

.

.

.

.

.

.

lecture 32:

Inverse-PINNs Problem Setting

Inverse Physics Informed Neural Networks (I-PINNs)

Buy nowLearn 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 Evaluation2