计算机建模作业代写|FIT3139 2023-S1: Final project

这是一篇来自澳洲的关于计算机建模作业代写,具体作业要求是:解释和应用计算科学模型的建立、验证和解释的过程;分析建模方法的核心类别之间的差异(数值与分析;线性与非线性;连续与离散;确定性与随机性);评估选择不同的建模方法的含义;合理化模拟和数据可视化在科学中的作用;将以上所有内容应用于解决跨各种科学学科的现实世界问题的理想化,以下是作业部分内容:

 

Task description

To demonstrate all learning outcomes, you will develop an extension of a model discussed in the classroom. An extension addresses the same problem, but adds or relaxes specific assumptions about the model. For example, taking a deterministic model and introducing assumptions to do a stochastic analysis,or providing stochastic analysis for a simulation.

Your extension should address the same problem, but contain some different assumptions that may or may not lead to different conclusions — an analysis should be presented comparing the results of the original model and the extended model. The model extension should be explained, interpreted an analysed,and it should allow you to showcase at least two of the following techniques:

  • Markov chains
  • Montecarlo simulation
  • Heuristics
  • Game theory

Your extension should address two different modelling questions, and use the algorithms, techniques and visualisations discussed in the clasroom to answer those questions.

Submission structure

Report structure

Your report should contain the following sections:

Section 1: Specification table

Fill the following table.

Important: This table should be briefly discussed and signed by your demonstrator on week 11 and week 12, during the lab session – not via email or forum post, please plan accordingly.

Section 2: Introduction

  • Learning outcomes 1, 5. 10% of project final mark
  • Identify the problem you want to solve and its motivation, describe what the extension will be and identify questions your model will answer. In other words, this section takes the information in the specification table and develops it providing more detail and a motivation of your questions, and how your techniques are appropriate.
  • Write clearly. Your mark is based on what we can understand so spend time crafting the text.

Section 3: Model description

  • Learning outcomes 1, 2, 5. 35% of project final mark
  • Specify model extension details and list assumptions for both the original model and the extension model. Determine the class of model and analysis you are presenting (Numerical versus Analytical;Linear versus Non-linear; Continuous versus Discrete; Deterministic versus Stochastic). Be sure to describe in detail any algorithms or mathematical results or derivations you may use.
  • Be clear and help the reader as much as you can.

Section 4: Results

  • Learning outcomes 2, 3, 4, 5. 35% of project final mark
  • Interpret and analyse the results of your extended model, including visualisation of results. You should explain how you arrive at your results. All figures should be discussed, explained and interpreted and your report should include at least 3 Figures. The results and figures should support how you are answering the questions you have chosen to answer.
  • Be clear and help the reader as much as you can.

Section 5: List of algorithms and concepts

  • Learning outcomes 2, 5. 5% of project final mark
  • List of algorithms and concepts used in the unit that play a role in your model and interpretation.

Video presentation

You should submit a presentation where you discuss your extended model. The presentation should be no longer that 10 minutes, and use slides to enhance the description of the model and the explanation of your results. It is suggested the presentation keep a similar structure to that of the report. The presentation is worth 15% of project final mark.

A simple procedure to record the presentation using zoom can be found here: https://www.youtube.com/watch?v=P6cTbnUPwfY

Source code

All code should be submitted and appropriately commented. It will be checked for correctness and be part of the marking in the model section (if the code is used to produce results, or in the results section if the code is used to analyze results). Clarity is in your best interest.

You can use any of the standard libraries we used in the class as long as you can explain what the library is doing.

……

 

任务描述

为了展示所有学习成果,您将开发课堂上讨论的模型的扩展。 扩展解决了同样的问题,但增加或放宽了关于模型的特定假设。 例如,采用确定性模型并引入假设来进行随机分析,或者为模拟提供随机分析。

你的扩展应该解决同样的问题,但包含一些不同的假设,这些假设可能会或可能不会导致不同的结论——应该提供一个分析来比较原始模型和扩展模型的结果。 应该解释、解释和分析模型扩展,并且它应该允许您展示至少以下两种技术:

马尔可夫链
蒙特卡洛模拟
启发式
博弈论

您的扩展应该解决两个不同的建模问题,并使用课堂上讨论的算法、技术和可视化来回答这些问题。

提交结构

报告结构

您的报告应包含以下部分:

第一节:规格表

填写下表。

重要提示:在第 11 周和第 12 周的实验课程期间,您的演示者应简要讨论并签署此表——不是通过电子邮件或论坛帖子,请相应地计划。

第 2 部分:简介

学习成果 1、5。占项目最终分数的 10%
确定您要解决的问题及其动机,描述扩展的内容并确定您的模型将回答的问题。 换句话说,这部分采用规格表中的信息并对其进行开发,以提供更多细节和问题的动机,以及您的技术如何适用。
写清楚。 您的标记基于我们可以理解的内容,因此请花时间制作文本。

第 3 节:模型说明

学习成果 1、2、5。项目最终分数的 35%
指定模型扩展细节并列出原始模型和扩展模型的假设。 确定您要呈现的模型和分析的类别(数值与分析;线性与非线性;连续与离散;确定性与随机性)。 请务必详细描述您可能使用的任何算法或数学结果或推导。
清楚并尽可能地帮助读者。

第 4 节:结果

学习成果 2、3、4、5。项目最终分数的 35%
解释和分析扩展模型的结果,包括结果可视化。 你应该解释你是如何得出结果的。 应对所有数字进行讨论、解释和解释,并且您的报告应至少包括 3 个数字。 结果和数字应该支持你如何回答你选择回答的问题。
清楚并尽可能地帮助读者。

第 5 节:算法和概念列表

学习成果 2, 5. 项目最终分数的 5%
在您的模型和解释中发挥作用的单元中使用的算法和概念列表。

视频演示

您应该提交一份讨论您的扩展模型的演示文稿。 演示应该不再是 10 分钟,并使用幻灯片来增强模型的描述和结果的解释。 建议演示文稿保持与报告类似的结构。 演示文稿占项目最终分数的 15%。

可以在此处找到使用缩放记录演示文稿的简单程序:https://www.youtube.com/watch?v=P6cTbnUPwfY

源代码

应提交所有代码并适当注释。 它将检查正确性并成为模型部分标记的一部分(如果代码用于生成结果,或者如果代码用于分析结果,则在结果部分)。 清晰对您最有利。

您可以使用我们在课堂上使用的任何标准库,只要您能解释该库的作用即可。

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