|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/inverse-physics-informed-neural-networks-ipinns/
课程评论：没有评论
课程名称：逆物理信息神经网络（I-PINNs） 课程概述：本课程旨在使您掌握有效利用逆物理信息神经网络（IPINNs）的技能。我们将深入探讨解决偏微分方程（PDEs）的基本概念，并展示如何通过应用逆物理信息神经网络来计算模拟参数，数据通过有限差分法（FDM）解决PDEs生成。在本课程中，您将学习以下技能： - 理解有限差分法的数学原理。 - 从零开始编写和构建算法来解决有限差分法问题。 - 理解偏微分方程（PDEs）的数学原理。 - 使用Pytorch编写和构建机器学习算法以解决逆-PINNs。 - 使用DeepXDE编写和构建机器学习算法以解决逆-PINNs。 课程内容包括： - Pytorch矩阵和张量基础知识。 - 一维Burgers方程的有限差分法数值解。 - 一维Burgers方程的物理信息神经网络（PINNs）解法。 - 一维Burgers方程的总变差减小（TVD）方法解法。 - 一维Burgers方程的逆-PINNs解法。 - 使用DeepXDE求解二维Navier-Stokes方程的逆-PINNs。 如果您缺乏机器学习或计算工程的先前经验，请不要担心，因为本课程全面且内容丰富，为您提供机器学习、偏微分方程（PDEs）和逆物理信息神经网络（IPINNs）的基本知识。让我们一同享受学习PINNs的乐趣！
This comprehensive course is designed to equip you with the skills to effectively utilize Inverse Physics-Informed Neural Networks (IPINNs). We will delve into the essential concepts of solving partial differential equations (PDEs) and demonstrate how to compute simulation parameters through the application of Inverse Physics Informed Neural Networks using data generated by solving PDEs with the Finite Difference Method (FDM).In this course, you will learn the following skills:Understand the Math behind Finite Difference Method.Write and build Algorithms from scratch to sole the Finite Difference Method.Understand the Math behind partial differential equations (PDEs).Write and build Machine Learning Algorithms to solve Inverse-PINNs using Pytorch.Write and build Machine Learning Algorithms to solve Inverse-PINNs using DeepXDE.We will cover:Pytorch Matrix and Tensors Basics.Finite Difference Method (FDM) Numerical Solution for 1D Burgers Equation.Physics-Informed Neural Networks (PINNs) Solution for 1D Burgers Equation.Total variation diminishing (TVD) Method Solution for 1D Burgers Equation.Inverse-PINNs Solution for 1D Burgers Equation.Inverse-PINNs for 2D Navier Stokes Equation using DeepXDE.If you lack prior experience in Machine Learning or Computational Engineering, please dont worry. as This course is comprehensive and course, providing a thorough understanding of Machine Learning and the essential aspects of partial differential equations PDEs and Inverse Physics Informed Neural Networks IPINNs. Let's enjoy Learning PINNs together
