Скачать с ютуб Learning Physics Informed Machine Learning Part 1- Physics Informed Neural Networks (PINNs) в хорошем качестве

Learning Physics Informed Machine Learning Part 1- Physics Informed Neural Networks (PINNs)

This video is a step-by-step guide to solving a time-dependent partial differential equation using a PINN in PyTorch. Since the GPU availability could be a problem, we will be using Google Colab. Also, if you are interested and PINNs and Machine Learning, please consider subscribing to the Crunch Group (Brown University) Youtube channel. They upload weekly seminars on Scientific Machine Learning.    / @crunchgroup   LINKS Code (Github): https://github.com/jdtoscano94/Learni... You will find a few more examples and our latest research on our state-of-the-art method called residual-based attention here: https://github.com/jdtoscano94/Learni... TIMESTAMPS 00:00 Introduction 00:20 Problem Setup (Diffusion Equation) 00:43 Introduction to PINNs 02:23 Libraries 02:53 Data Generation 05:11 Data Preparation 07:26 Training Data (Initial and Boundary Conditions) 12:52 Training Data (Collocation Points for our PDE) 15:02 Coding our Physics Informed Neural Network 18:16 Training our Physics Informed Neural Network 23:34 Results Thank you so much for watching!