Mohammad Samara/Simulation By Deep Neural Operator (DeepONets)

Simulation By Deep Neural Operator (DeepONets)

  • Course
  • 42 Lessons

Simulations with AI Using DATA ONLY

  • 8.5 hours on-demand video

  • Certificate of completion

  • Full access

55$ Special Discount price : 10$

What you'll learn

  • Understand the Theory behind deep neural operator equations solvers.

  • Build DeepONet based deep neural operator solver.

  • Build an deep neural operator code using DeepXDE.

  • Build an deep neural operator code using Pytorch.

Contents

Section 1: Introduction

lecture 1: Introduction
Preview
lecture 2: Install PyTorch / CUDA
lecture 3: Course structure
Preview
lecture 4: Deep Neural Operator
Preview
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 Evaluation

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
Preview
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
Preview
lecture 29: Training Process
lecture 30: Results Evaluation

Section 7: 2D Fluid Neural Operator using DeepXDE

Section-7 Files
lecture 31: Data creation
lecture 32: Data Preprocessing
lecture 33: Model Build Up
lecture 34: Training Process
lecture 35: Results Evaluation

Requirements

  • High School Math

  • Basic Python knowledge

Description

This comprehensive course is designed to equip you with the skills to effectively utilize Simulation By Deep Neural Operators. We will delve into the essential concepts of solving partial differential equations (PDEs) and demonstrate how to build a simulation code through the application of Deep Operator Network (DeepONet) using data generated by solving PDEs with the Finite Difference Method (FDM).

In this course, you will learn the following skills:

  • Understand the Math behind Finite Difference Method.

  • Write and build Algorithms from scratch to sole the Finite Difference Method.

  • Understand the Math behind partial differential equations (PDEs).

  • Write and build Machine Learning Algorithms to build Simulation code By Deep Neural Operators using Pytorch.

  • Write and build Machine Learning Algorithms to build Simulation code By Deep Neural Operators using DeepXDE.

  • Compare the results of Finite Difference Method (FDM) with the Deep Neural Operator using the Deep Operator Network (DeepONet).

We will cover:

  • Pytorch Matrix and Tensors Basics.

  • Finite Difference Method (FDM) Numerical Solution for 1D Heat Equation.

  • Deep Neural Operator to perform integration of an Ordinary Differential Equations(ODE).

  • Deep Neural Operator to perform simulation for 1D Heat Equation using Pytorch.

  • Deep Neural Operator to perform simulation for 1D Heat Equation using DeepXDE.

  • Deep Neural Operator to perform simulation for 2D Fluid Motion using DeepXDE.

If you lack prior experience in Machine Learning or Computational Engineering, please dont worry. as this course is comprehensive and course, providing a thorough understanding of Machine Learning and the essential aspects of partial differential equations PDEs and Simulation By Deep Neural Operators by applying Deep Operator Network (DeepONet) .

Let's enjoy Learning PINNs together

Hear from our happy students

"very well and clearly explained! I love it!"

Florian Gartner

"very clear."

Nam Nguyen

"Awesome!."

Conrad Salinas

"Thank you!Dr.Samara!I just started studying this topic ,and you really made this easy for me ! Thank you !"

Ninfa