Inverse Physics Informed Neural Networks (I-PINNs)

🎓 Master the Art of Modeling Physical Systems with AI: Inverse Physics-Informed Neural Networks (I-PINNs) 🚀

Course Title: Model Physical Systems Parameters With AI

Headline: Unleash the Power of Machine Learning to Compute Simulation Parameters for Physical Systems Through Inverse PINNs! 🌫️⚛️

Welcome to our engaging and comprehensive course on Inverse Physics-Informed Neural Networks (I-PINNs)! This isn't just another online class; it's a deep dive into the fascinating world where physics meets AI. Led by the esteemed Dr. Mohammad Samara, you'll learn to harness the predictive power of Inverse PINNs to solve real-world problems by computing simulation parameters through artificial intelligence.

Course Overview:

In this course, you will embark on a journey through the following key skill areas:

• Understanding Finite Difference Method (FDM): You'll grasp the fundamental concepts behind solving partial differential equations (PDEs) numerically using FDM.

• Algorithm Development: Learn to write and build algorithms from scratch to solve problems using the Finite Difference Method.

• Mastering PDEs: Delve into the mathematics of PDEs and understand how they govern natural phenomena.

• Machine Learning for Inverse-PINNs: Get hands-on experience with writing and building machine learning algorithms to solve Inverse-PINNs using popular libraries like Pytorch and DeepXDE.

Curriculum Highlights:

• Pytorch Basics: Gain a solid foundation in using PyTorch for matrix and tensor operations, which are the building blocks of numerical computations.

• FDM Solution for 1D Burgers Equation: Learn how to numerically solve the 1D Burgers equation using the Finite Difference Method.

• PINNs Solution for 1D Burgers Equation: Explore the concept of Physics-Informed Neural Networks and their application to solving the 1D Burgers equation.

• TVD Method for 1D Burgers Equation: Understand and implement the Total Variiation Diminishing (TVD) method, a critical technique in maintaining the stability and accuracy of numerical solutions.

• Inverse-PINNs for 1D Burgers Equation: Discover how to approach problems by learning from the solutions rather than traditional forward problem-solving methods.

• Inverse-PINNs for 2D Navier-Stokes Equation Using DeepXDE: Apply your knowledge to tackle more complex problems, such as fluid dynamics represented by the 2D Navier-Stokes equation using advanced tools like DeepXDE.

Why Enroll?

This course is meticulously designed for learners of all levels. Whether you're a beginner in machine learning or computational engineering, this comprehensive curriculum will equip you with a thorough understanding of the subject matter. With a focus on hands-on learning and practical applications, you'll be well-prepared to model physical systems parameters with AI.

Join us on this exciting intellectual adventure as we explore the interplay between physics and artificial intelligence. Let's dive into the world of Inverse PINNs together! 🤖💫