Description
Simulation by Deep Neural Operators (Deponets), This comprehensive course is designed to equip you with the skills to effectively use Simulation by Deep Neural Operators. We will delve deep into the essential concepts of solving partial differential equations (PDEs) and demonstrate how to create simulation code through the application of Deep Operator Networks (Deponets) using data generated by solving PDEs with the Finite Difference Method (FDM). If you lack prior experience in machine learning or computational engineering, please do not worry. As this course is comprehensive and course provides an in-depth understanding of the essential aspects of machine learning and partial differential equations PDEs and Simulation by Deep Neural Operators by applying Deep Operator Networks (Deponets).
What you will learn
- Understand the theory behind the deep neural operator equation solver.
- Build a DeepNet-based deep neural operator solver.
- Create a deep neural operator code using DeepXDE.
- Create a deep neural operator code using Pytorch.
Who is this course for
- Engineers and programmers who want to learn how to do simulations via Deep Neural Operators
Specification of Simulation by Deep Neural Operators (Deponets)
- Publisher: Udemy
- Teacher: Dr.Mohammad Samara
- language English
- Level: All Levels
- No. of Courses: 36
- Duration: 8 hours 28 minutes
Content of simulations by deep neural operators (Deponets)
Requirements
- High School Mathematics
- Basic Python knowledge
Pictures
Sample clip
installation Guide
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Quality: 720p
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file size
10.98GB