0. Summary

1. Problem description, background analysis, etc.

2. Model assumptions and symbolic descriptions (list)

3. Model establishment: problem analysis, quoting mathematical propositions, formula derivation, model ⅰ, model ⅱ, etc.

4. Solution of the model: design or selection of calculation method, calculation steps (block diagram), name of software adopted, etc.

5. Results of the model: error analysis, model verification. ...

6. Model evaluation: characteristics, advantages and disadvantages, improvement methods and popularization.

7. Reference

8. Appendix: charts, procedures, etc.

Second, the explanation of some basic contents.

0. Summary

It plays an important role in the evaluation of the whole paper, so you must write carefully (the length cannot exceed one page). During the national review, the quality of the papers will be preliminarily screened according to their abstracts, overall structure and general situation. Poor writing, unclear arguments, unclear organization, the judges no longer read the text, and the paper was eliminated.

Abstract is the essence of the full text, and it should be pointed out that:

Mathematical classification of (1) model (what type does it belong to mathematically, such as dynamic programming, stability of differential equations, etc. )

(2) the concept of modeling (ideas)

(3) Algorithm idea (solution idea)

(4) Model characteristics (model advantages and disadvantages, algorithm characteristics, result test, sensitivity analysis, model test, etc.). )

(5) Main results (numerical results and conclusions) (answer all the "questions" raised by the topic)

Note that sentences must be accurate, concise, fluent and neat, and must be carefully proofread.

1. Restatement of the problem

Briefly restate the original question, but don't copy it, but restate it from a mathematical point of view.

2. Model assumptions

According to the scoring principle, the rationality of basic assumptions accounts for an important proportion.

In order to make reasonable assumptions according to the conditions and requirements in the topic, the key assumptions are indispensable to meet the meaning of the topic.

3. Establishment of the model

(1) Mathematical modeling is to solve problems by mathematical methods. First of all, there must be mathematical models: mathematical formulas, equations, schemes, etc. Require completeness, correctness and conciseness.

(2) The model should be practical and effective, based on the principle of effectively solving problems, and not pursue high (level) and difficult (degree) mathematics. If it can be solved by elementary methods, there is no need for advanced methods; If it can be solved by simple methods, there is no need for complicated methods; If you can use a method that most people can understand, you don't need a method that only a few people can understand.

(3) Encourage innovation, but be pragmatic. Mathematical model innovation can be embodied in the model (good ideas, good methods, good strategies, etc.). ); Model solving (good algorithm, good steps, good program); Expression of results (eye-catching, chart, analysis, test, etc.). ); This model is being popularized.

4. Model solving

(1) When a mathematical proposition needs to be established, the statement of the proposition should conform to the norms of the mathematical proposition and be as rigorous as possible.

(2) The principle, basis and steps of the algorithm need to be explained. If existing software is used, the reason and software name should be stated.

(3) In the calculation process, the intermediate results are unnecessary and need not be listed.

(4) Try to get a reasonable numerical result.

5. The results of the model

(1) The correctness or rationality of the final numerical results is the first;

(2) Check the numerical results or simulation results when necessary. When the result is incorrect, unreasonable or has a large error, analyze the reasons and modify and improve the algorithm, calculation method or model;

(3) The questions, numerical results and conclusions required to be answered in the topic must be listed one by one;

(4) Consider whether it is necessary to list multiple groups of data, and compare and analyze the data to provide a basis for proposing various schemes;

(5) The results should be centralized, eye-catching, intuitive and easy to compare and analyze.

(6) When necessary, discuss the answers to questions qualitatively or regularly. The final conclusion should be clear.

6. Model evaluation

(1) explain the characteristics, foster strengths and avoid weaknesses.

(2) Change the original requirements, and you can re-model here.

(3) when promoting or improving the direction, it should be reasonable and feasible, and don't play with new mathematical terms.

7. Reference

List according to regulations.

8. Appendix

(1) The main result data should be listed in the text.

(2) Data and tables can be listed here, but don't be wrong, and the wrong ones would rather not be listed.

Third, thinking and work planning before writing the answer sheet

There should be an overall arrangement in advance:

(1) What questions need to be answered on the answer sheet-what problems need to be solved in modeling;

(2) How to answer questions-how to express the results;

(3) What key data should be listed in each question-what key data should be calculated in modeling;

(4) For each quantity, list one or more groups of numbers-whether to calculate one or more groups of numbers. ...

List the items in one breath. Never think about it, write it there, it's messy.