PINNs Using NVIDIA Modulus
Pinns Using Nvidia Modulus
Published 2/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 11.88 GB | Duration: 9h 7m
Simulations with AI
What you'll learn
Build PINNs based pdes solver.
Understand the Theory behind PINNs PDEs solvers.
Build models using NVIDIA Modulus
Deploy NVIDIA Modulus useing GoogleColab and your own NVIDIA GPU
Requirements
High School Math
Basic Python knowledge
Description
DescriptionThis is a introductory course that will prepare you to work with Physics-Informed Neural Networks (PINNs) using NVIDIA Modulus. We will cover the fundamentals of Solving partial differential equations (PDEs) using Physics-Informed Neural Networks (PINNs) from its basics and March towards solving PINNs with Nvidia modulus.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.Write and build Machine Learning Algorithms to solve PINNs using Pytorch.Write and build Machine Learning Algorithms to solve PINNs using Nvidia Modulus.Postprocess the results.Use opensource libraries.Define your own PDEs to solve them or use built in equations (such as the N.S equations in Nvidia Modulus).We will cover:Basics of Pytorch.How to deploy Nvidia Modulus on your own computer GPU and in Google Collab.Physics-Informed Neural Networks (PINNs) Solution for 1D Burgers Equation using pytorch.Physics-Informed Neural Networks (PINNs) Solution for 1D wave Equation using Nvidia modulus.Physics-Informed Neural Networks (PINNs) Solution for cavity flow problem using Nvidia modulus.Physics-Informed Neural Networks (PINNs) Solution for 2D heat sink flow problem using Nvidia modulus.If you do not have prior experience in Machine Learning or Computational Engineering, that's no problem. This course is complete and concise, covering the fundamentals of Machine Learning/ Physics-Informed Neural Networks (PINNs). Let's enjoy Learning Nvidia Modulus together.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course Structure
Lecture 3 Installing Anaconda
Lecture 4 Deep Learning Theory
Lecture 5 PINNs Theory
Section 2: Pytorch Basics
Lecture 6 Install PyTorch / CUDA
Lecture 7 PyTorch Tensors Basics
Lecture 8 Tensors to NumPy arrays
Lecture 9 Backpropagation Theory
Lecture 10 Backpropagation using PyTorch
Section 3: PINNs Solution for 1D Burgers Equation with Pytorch
Lecture 11 Define the Neural Network
Lecture 12 Initial Conditions and Boundary Conditions
Lecture 13 Optimizer
Lecture 14 Loss Function
Lecture 15 Train the Model
Lecture 16 Results Evaluation
Section 4: 1D Wave Equation
Lecture 17 What is the Wave Equation
Lecture 18 Setting Up Google Colab
Lecture 19 Define the Wave Equation Function
Lecture 20 Define the Config File
Lecture 21 Import Needed Libraries
Lecture 22 Set Up the main RUN File
Lecture 23 Define the B.C, I.C, Interior Points
Lecture 24 Add Validator Functionality
Lecture 25 Solve
Lecture 26 Results Extraction
Lecture 27 Results Post Processing
Section 5: Cavity Flow
Lecture 28 Setting up env. in your personal computer
Lecture 29 Cavity Flow Problem
Lecture 30 Define the Config File
Lecture 31 Import Needed Libraries
Lecture 32 Set Up the main RUN File
Lecture 33 Define the Navier-Stokes equation and DNN
Lecture 34 Define the B.C, I.C, Interior Points
Lecture 35 Solve
Lecture 36 Results Extraction
Lecture 37 Results Post Processing
Lecture 38 Pretrained Model Inference
Section 6: 2D Heat Sink
Lecture 39 2d heat channel problem
Lecture 40 Define the Config File
Lecture 41 Import Needed Libraries
Lecture 42 Set Up the main RUN File
Lecture 43 Define the geometry
Lecture 44 Define the Navier-Stokes equation and DNN
Lecture 45 Define the B.C, I.C, Interior Points
Lecture 46 Add Monitor
Lecture 47 Solve
Lecture 48 Results Extraction
Lecture 49 Results Post Processing
Engineers and Programmers whom want to Learn PINNs,learn NVIDIA Modulus
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