Simulation By Deep Neural Operators (DeepONet)

[OneDDL]

Free Download Simulation By Deep Neural Operators (DeepONet)
Published 12/2023
Created by Dr.Mohammad Samara
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 36 Lectures ( 8h 28m ) | Size: 10.8 GB

Model Physical Systems using ONLY DATA
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.
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 coverytorch 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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