Physics Informed Neural Networks (PINNs) || Ordinary Differential Equations || Step-by-Step Tutorial

Video-ID-V20240808-AA In this tutorial, we'll explore how to solve the 1D Poisson equation using Physics Informed Neural Networks (PINNs). You'll learn about the fundamentals of the Poisson equation, the integration of physical laws into neural networks, and how to set up, build, and train a PINN to solve this important equation. Follow along with our hands-on coding examples and gain practical insights into applying PINNs for solving differential equations. Perfect for both beginners and advanced learners, this video provides a comprehensive yet straightforward approach to mastering PINNs. Physics Informed Neural Networks PINNs, Rethinking Physics Informed Neural Networks, Physics Informed Machine Learning- High Level Overview of AI and ML in Science and Engineering, Introduction to Physics Informed Neural Networks - A hands on, project based course, Physics Informed Neural Networks PINNs - An Introduction, Learning Physics Informed Machine Learning, How Do Physics-Informed Neural Networks Work?, Physics-informed neural networks for fluid mechanics, Get hands On with PINNs, Physics Informed Neural Networks - an intuitive explanation - Introduction, Physics Informed Neural Networks PINNs for approximating PDEs, Physics Informed Neural Network PINNs: Step by Step plan, Inverse Physics Informed Neural Networks I-PINNs, Physics-informed neural networks for fluid mechanics, Neural Network to Solve Navier-Stokes Equations. 📂 *Get This Tutorial Project Files* - Download the exact files used in this tutorial and practice hands-on. *Link* : https://topmate.io/epyconnect/1827585 - *Want to go beyond this single project?* → This tutorial is part of our full course on *Physics Informed Neural Networks Masterclass* , which includes lifetime access to all video lessons and complete source code for every algorithm. *Link* : https://exly.co/PvxFUL Subscribe to our channel for more exciting machine learning tutorials. Join us on this journey to unlock the potential of machine learning in civil engineering applications.