Descriptions
NVIDIA Modulus: advanced topics. This course deals with advanced topics related to PINN using NVIDIA Modulus. We will cover the topics of inverse PINNs, deep neural operator networks with DeepONet, deep neural operator networks using Fourier neural operator (FNO), PINN for 3D linear elasticity problem, PINN for multi-domain computing and geometric optimization using PINN. Learn the mathematics behind solving partial differential equations (PDE) using PINN, I-PINN, deep neural operator network for DeepONet, as well as FNO, multi-domain calculation, and finally geometric optimization using PINN. If you don’t have a background in machine learning or computer engineering, this isn’t a problem. However, it is recommended to have basic knowledge of how to use and run code using Nvidia Modulus.
What will you learn
- I-PINN for a two-dimensional heat sink flow problem.
- DeepONEt for the integration problem.
- Neural Fourier operator FNO for the Darcy problem.
- PINN for a three-dimensional linear elasticity problem.
- PINN for multi-domain 3D fluid/solid calculations.
- PINN for 3D geometric optimization of heat exchanger flow problem.
Who is this course for?
- Engineers and programmers who want to learn PINN
- Explore additional NVIDIA Modulus topics
NVIDIA Modulus Specifications: Additional Topics
- Publisher: Udemy
- Teacher: Dr. Mohammad Samara
- English language
- Level: Intermediate
- Number of courses: 66
- Duration: 10 hours 5 minutes
Contents NVIDIA Modulus: Additional Topics
Requirements
- High School Mathematics
- Basic knowledge of Python
Images
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Installation instructions
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Quality: 720p
Download links
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file size
12.98 GB