Physics-Nemo [Modulus ] : Advanced Topics

Description

This course is related with Advanced topics related with PINNs using The library of Modulus [Physics-Nemo]. We will cover the topics of Inverse PINNs, Deep Neural Operator Network with DeepONet, Deep Neural Operator Network using Fourier Neural Operator (FNO), PINN for 3D Linear Elasticity Problem, PINNs for Multi Domain Calculation, and Geometric Optimization using PINNs.

What skills will you Learn:

In this course, you will learn the following skills:

• Understand the Math behind solving partial differential equations (PDEs) with PINNs, I-PINNs,  Deep Neural Operator Network for DeepONet, along with FNO, Multi Domain Calculation and finally Geometric Optimization using PINNs.

• Write and build Machine Learning Algorithms to solve PINNs using The library of Modulus [Physics-Nemo].

• Postprocess the results.

• Pre-process the data and upload it to The library of Modulus [Physics-Nemo].

• Use opensource libraries.

  
  

We will cover:

• Inverse Physics-Informed Neural Networks (I-PINNs) Solution for 2D heat sink flow problem .

• Deep Neural Operator Network (DeepONet) Solution for  Integration problem.

• Deep Neural Operator Network Fourier Neural Operator (FNO) Solution for  Darcy problem.

• Physics-Informed Neural Networks (PINNs) Solution for   3D Linear Elasticity Problem.

• Physics-Informed Neural Networks (PINNs) Solution for   3D Fluid/ Solid Multi Domain Calculation.

• Physics-Informed Neural Networks (PINNs) Solution for 3D Geometric Optimization for Heat Exchanger Flow Problem.

If you do not have prior experience in Machine Learning or Computational Engineering, that's no problem. However it is recommended to have knowledge in the basics of the use and code running using The library of Modulus [Physics-Nemo].

Let's enjoy Learning The library of Modulus [Physics-Nemo] together.