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FIT3139 computational scientific model
创建日期：2024-08-28 17:50:51
标签： FIT3139
computational scientific model

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FIT3139 


This final project has the purpose of assessing all learning outcomes in the unit. The learning outcomes are as follows:

1.  Explain and apply the process of computational scientific model building, verification and interpretation;

2.  Analyse the differences  between  core classes of modelling approaches  (Numerical versus Analytical; Linear versus Non-linear; Continuous versus Discrete; Deterministic versus Stochastic);

3.  Evaluate the implications of choosing different modelling approaches;

4.  Rationalise the role of simulation and data visualisation in science;

5.  Apply all of the above to solving idealisations of real-world problems across various scientific disciplines.

What to submit

The final report will consist of two parts. A video presentation (worth 15% of the project mark) and a final written report worth (85% of the project mark). We also require all the source code, appropiately documented via comments as well as the slides  used for the  presentation.   The  weights  on the different sections of the report are futher discussed below.

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:

? Gillespie

? 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

Excluding code your report should be no longer than 15 pages.  Your report should contain the following sections:

Section 1:  Specification table

Fill the following table.

Base model

One sentence description of the base model

Extension assumptions

One paragraph description on how assumptions are modified and the nature of the extension

Techniques showcased

Technique 1. Technique 2.

Modelling question 1

Questions being addressed.

Modelling question 2


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.

Feedback opportunities

? Workshop  1  of  week 9 will  discuss  the  project  task  and  provide examples.    There  will  be  no pre-workshop video, use the time to start thinking about what you want to do.

? Week 10’s applied: You are welcome to have a very brief discussion of topic with lab demonstrator

– they can provide simple advise con how to fine tune your question or idea.

? Week  11’s  applied:  Present a draft of the specification table to your demonstrator and explain what you expect in terms of results.

? Week 12’s applied:  Discuss your progress with your demonstrator.

We will also have extra consultations before the due date, but they do not replace the activities above. Plan ahead and good luck.

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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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