Скачать с ютуб Physics Informed Neural Networks (PINNs) for Solving System of ODEs || TensorFlow || SciML в хорошем качестве

Physics Informed Neural Networks (PINNs) for Solving System of ODEs || TensorFlow || SciML

Video ID - V48 In this tutorial, we dive into the exciting world of Physics-Informed Neural Networks (PINNs) and how they can be used to solve a system of ordinary differential equations (ODEs). We’ll explore the theory behind PINNs and walk you through the practical steps of implementing them using TensorFlow. By the end of this video, you’ll have a clear understanding of how to set up and train a neural network to approximate solutions for ODEs, as well as how to verify these solutions against analytical methods like Laplace transforms. Key topics covered: - Introduction to PINNs and how they differ from traditional neural networks - Setting up a neural network architecture for ODEs - Formulating the loss function using residuals and initial conditions - Training the network using TensorFlow and optimizing the loss - Comparing PINN solutions with analytical solutions - Tips on adjusting hidden layers, neurons, and collocation points for better accuracy This video is perfect for students, researchers, and engineers interested in using deep learning to solve differential equations. Whether you are new to neural networks or looking to enhance your understanding of PINNs, this tutorial is packed with valuable insights and practical coding examples. Keywords - 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. Subscribe to our channel for more exciting machine learning tutorials. Join us on this journey to unlock the potential of machine learning in many engineering applications. #sciml #physicsinformedneuralnetworks