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所在平台: Udemy |
课程主页: https://www.udemy.com/course/simulation-by-deep-neural-operators-deeponet/
课程评论：没有评论
课程名称：深度神经算子模拟（DeepONets） 课程概述：本课程旨在帮助您有效运用深度神经算子进行模拟。我们将深入探讨解决偏微分方程（PDEs）的基本概念，并通过应用深度算子网络（DeepONet）展示如何通过有限差分法（FDM）生成的数据构建模拟代码。在本课程中，您将学习以下技能：理解有限差分法的数学基础；从零开始编写并构建算法来解决有限差分法；理解偏微分方程（PDEs）的数学基础；使用Pytorch编写和构建机器学习算法来创建深度神经算子模拟代码；使用DeepXDE编写和构建机器学习算法来创建深度神经算子模拟代码；比较有限差分法（FDM）与使用深度算子网络（DeepONet）进行的深度神经算子的结果。 我们将涵盖的内容包括： - Pytorch矩阵和张量基础 - 一维热方程的有限差分法（FDM）数值解 - 深度神经算子用于常微分方程（ODE）积分 - 使用Pytorch进行一维热方程模拟的深度神经算子 - 使用DeepXDE进行一维热方程模拟的深度神经算子 - 使用DeepXDE进行二维流体运动模拟的深度神经算子 如果您在机器学习或计算工程方面没有 prior 经验，请不必担心。本课程内容全面，并提供对机器学习、偏微分方程（PDEs）及深度神经算子模拟的深入理解。让我们一起享受学习PINNs的乐趣吧！
This comprehensive course is designed to equip you with the skills to effectively utilize Simulation By Deep Neural Operators. We will delve into the essential concepts of solving partial differential equations (PDEs) and demonstrate how to build a simulation code through the application of Deep Operator Network (DeepONet) 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 build Simulation code By Deep Neural Operators using Pytorch.Write and build Machine Learning Algorithms to build Simulation code By Deep Neural Operators using DeepXDE.Compare the results of Finite Difference Method (FDM) with the Deep Neural Operator using the Deep Operator Network (DeepONet).We will cover:Pytorch Matrix and Tensors Basics.Finite Difference Method (FDM) Numerical Solution for 1D Heat Equation.Deep Neural Operator to perform integration of an Ordinary Differential Equations(ODE).Deep Neural Operator to perform simulation for 1D Heat Equation using Pytorch.Deep Neural Operator to perform simulation for 1D Heat Equation using DeepXDE.Deep Neural Operator to perform simulation for 2D Fluid Motion 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 Simulation By Deep Neural Operators by applying Deep Operator Network (DeepONet). Let's enjoy Learning PINNs together
