Simulation By Deep Neural Operator (DeepONets)

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Dr.Mohammad Samara

8:28:55

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1. Introduction

1. Introduction.mp4

05:02

2. Installing Anaconda.mp4

05:46

3. Course structure.mp4

07:23

4. Deep Neural Operator.mp4

22:14

2. Pytorch Basics

1. Deep Learning Theory.mp4

11:32

2.1 links.txt

2. Install PyTorch CUDA.mp4

04:29

3.1 basic 1 main.zip

3. PyTorch Tensors Basics.mp4

18:53

4.1 basic 2 main.zip

4. Tensors to NumPy arrays.mp4

08:20

5. Backpropagation Theory.mp4

16:59

6.1 basic bp main.zip

6. Backpropagation using PyTorch.mp4

05:54

3. FDM Numerical Solution 1D Heat Equation

1.1 heat main.zip

1. Numerical solution theory.mp4

12:54

2.1 heat main.zip

2. Pre-processing.mp4

16:42

3.1 heat main.zip

3. Solving the Equation.mp4

10:34

4.1 heat main.zip

4. Post-processing.mp4

04:56

4. ODE Integration Neural Operator using PyTorch

1.1 ode int main.zip

1. Data creation.mp4

26:32

2.1 ode int main.zip

2. Data Preprocessing - Part 1.mp4

22:50

3.1 ode int main.zip

3. Data Preprocessing - Part 2.mp4

15:05

4.1 ode int main.zip

4. Model Build Up.mp4

32:36

5.1 ode int main.zip

5. Training Process.mp4

09:35

6.1 ode int main.zip

6. Results Evaluation.mp4

20:51

5. 1D Heat Equation Neural Operator using PyTorch

1.1 1d heat deeponet main.zip

1. Data creation.mp4

32:23

2.1 1d heat deeponet main.zip

2. Data Preprocessing - Part 1.mp4

17:31

3.1 1d heat deeponet main.zip

3. Data Preprocessing - Part 2.mp4

10:53

4.1 1d heat deeponet main.zip

4. Model Build Up.mp4

16:49

5.1 1d heat deeponet main.zip

5. Training Process.mp4

12:21

6.1 1d heat deeponet main.zip

6. Results Evaluation.mp4

21:10

6. 1D Heat Equation Neural Operator using DeepXDE

1.1 1d heat deeponet deepxde main.zip

1. Data creation.mp4

05:13

2.1 1d heat deeponet deepxde main.zip

2. Data Preprocessing.mp4

13:09

3.1 1d heat deeponet deepxde main.zip

3. Model Build Up.mp4

04:34

4.1 1d heat deeponet deepxde main.zip

4. Training Process.mp4

02:20

5.1 1d heat deeponet deepxde main.zip

5. Results Evaluation.mp4

17:14

7. 2D Fluid Neural Operator using DeepXDE

1.1 NS data 10.mp4

00:03

1.2 ns deeponet main.zip

1.3 use data.zip

1. Data creation.mp4

20:37

2.1 ns deeponet main.zip

2. Data Preprocessing.mp4

31:38

3.1 ns deeponet main.zip

3. Model Build Up.mp4

03:42

4.1 ns deeponet main.zip

4. Training Process.mp4

02:08

5.1 ns deeponet main.zip

5. Results Evaluation.mp4

18:03

Description

Simulations with AI Using DATA ONLY

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.

Who is this for?

Engineers and Programmers whom want to Learn to perform simulation via a deep neural operator

What You Need to Know?

High School Math

Basic Python knowledge

More details

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)Â 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
Who this course is for:
Engineers and Programmers whom want to Learn to perform simulation via a deep neural operator

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11 Years experience in Computation Models building, since 7 year I am mainly focused on the use of Data to build machine learning based models, to solve practical Industry problems such as data analysis, Value prediction, performance classification (abnormality detection), and applying Machine Learning to machine vision problems as well as system control and behavior mapping via Reinforcement learning using data collected from the testing and simulation.I Have a Phd and masters From the University of Tokyo, and worked in several Japanese and International companies, currently I am working in Panasonic as a Data Science/Machine Learning Expert (Job Rank: Chief Engineer).I am absolutely passionate about Data Science use in Engineering companies and I am looking forward to sharing my passion and knowledge with you!

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