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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/modeling-simulation-natural-processes
课程评论：没有评论
课程名称：自然过程的模拟与建模 课程概述：本课程介绍了适用于多种自然现象的建模方法和模拟工具。所展示的不同方法可以应用于流体运动、恒星动力学、种群演化等广泛主题。课程并不深入探讨任何特定的数值方法或过程，也不提供解决特定问题的配方，而是为学习者提供了关于不同方法论的基本指导，以帮助选择最适合的解决方案。课程作业将尽可能地实践，允许学生从零开始创建简短程序以解决简单问题。尽管课程中将广泛使用编程，但不要求学生具备高级编程经验。 课程大纲： 1. **导论与一般概念**：概述课程内容，并呈现建模与模拟的基本概念，重点讨论空间和时间的概念表示。介绍复杂系统建模，并通过对巨大动脉瘤的生长与血栓形成的模拟进行分析。首个建模方法——蒙特卡洛方法将被介绍。 2. **Python 3编程入门**：提供高性能计算的基本概念，教授Python 3的基础知识，该语言将用于本课程的测验。 3. **动力系统与数值积分**：讲解动力系统建模，这是研究时空依赖问题的主要方法。将展示如何将自然现象转化为数学方程并解决这些方程。 4. **元胞自动机**：定义元胞自动机的概念，并概述其基本构建模块，探讨该技术在自然现象中的应用，最后介绍一种流体流动模型的子类——格子气体自动机。 5. **流体流动的格子Boltzmann建模**：介绍格子Boltzmann方法，此为计算流体动力学的强大工具。通过实践步骤展示如何为该方法编写程序，并解决流体动力学中有趣的问题——障碍物后方的涡街模拟。 6. **粒子与点状物体**：简要回顾经典力学及数值方法，分析如何集成相互作用粒子的运动方程，介绍如何降低模拟系统中粒子相互作用所需的计算开销，重点讲解Barnes-Hut算法，这是一种流行的N体问题解决方案。 7. **离散事件模拟入门**：探索一种替代方法，建模在大多数时候表现出平凡行为但在离散事件序列下可能显著变化的系统。该方法最初用于排队理论系统的模拟，但已扩展至交通交叉口建模或火山灾害预测等多种问题。 8. **基于代理的模型**：利用基于代理的模型（ABM）分解复杂系统，关注代理与环境之间及彼此之间的关系，该方法源于人工智能研究，目前用于建模行人行为、社会昆虫、生物细胞等多种系统。 该课程适合希望了解自然现象建模与模拟基础的学生，提供了实践经验与理论知识，帮助他们掌握解决各类自然科学问题的工具。
Name:Introduction and general concepts
Description:This module gives an overview of the course and presents the general ideas about modeling and simulation. An emphasis is given on ways to represent space and time from a conceptual point of view. An insight of modeling of complex systems is given with the simulation of the grothw and thrombosis of giant aneurysms. Finally, a first class of modeling approaches is presented: the Monte-Carlo methods.
Name:Introduction to programming with Python 3
Description:This module intends to provide the most basic concepts of high performance computing used for modeling purposes. It also aims at teaching the basics of Python 3 which will be the programming language used for the quizzes in this course.
Name:Dynamical systems and numerical integration
Description:Dynamical systems modeling is the principal method developed to study time-space dependent problems. It aims at translating a natural phenomenon into a mathematical set of equations. Once this basic step is performed the principal obstacle is the actual resolution of the obtained mathematical problem. Usually these equations do not possess an analytical solution and advanced numerical methods must be applied to solve them. In this module you will learn the basics of how to write mathematical equations representing natural phenomena and then how to numerically solve them.
Name:Cellular Automata
Description:This module defines the concept of cellular automata by outlining the basic building blocks of this method. Then an insight of how to apply this technique to natural phenomena is given. Finally the lattice gas automata, a subclass of models used for fluid flows, is presented.
Name:Lattice Boltzmann modeling of fluid flow
Description:This module provides an introduction to the lattice Boltzmann method, a powerful tool in computational fluid dynamics. The lesson is practice oriented and show, step by step, how to write a program for the lattice Boltzmann method. The program is used to showcase an interesting problem in fluid dynamics, the simulation of a vortex street behind an obstacle.
Name:Particles and point-like objects
Description:A short review of classical mechanics, and of numerical methods used to integrate the equations of motions for many interacting particles is presented. The student will learn that the computational expense of resolving all interaction between particles poses a major obstacle to simulating such a system. Specific algorithms are presented to allow to cut down on computational expense, both for short-range and large-range forces. The module focuses in detail on the Barnes-Hut algorithm, a tree algorithm which is popular a popular approach to solve the N-Body problem.
Name:Introduction to Discrete Events Simulation
Description:In this module, we will see an alternative approach to model systems which display a trivial behaviour most of the time, but which may change significantly under a sequence of discrete events. Initially developed to simulate queue theory systems (such as consumer waiting queue), the Discrete Event approach has been apply to a large variety of problems, such as traffic intersection modeling or volcanic hazard predictions.
Name:Agent based models
Description:Agent Based Models (ABM) are used to model a complex system by decomposing it in small entities (agents) and by focusing on the relations between agents and with the environment. This approach is derived from artificial intelligence research and is currently used to model various systems such as pedestrian behaviour, social insects, biological cells, etc.
This course gives you an introduction to modeling methods and simulation tools for a wide range of natural phenomena. The different methodologies that will be presented here can be applied to very wide range of topics such as fluid motion, stellar dynamics, population evolution, ... This course does not intend to go deeply into any numerical method or process and does not provide any recipe for the resolution of a particular problem. It is rather a basic guideline towards different methodologies that can be applied to solve any kind of problem and help you pick the one best suited for you. The assignments of this course will be made as practical as possible in order to allow you to actually create from scratch short programs that will solve simple problems. Although programming will be used extensively in this course we do not require any advanced programming experience in order to complete it.
