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