| Program / Major | mDP | Goals |
|---|---|---|
| IoT Course | DP-4a・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Software Course | DP-4b・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Media Course | DP-4c・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Data Science Course | DP-4d・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Mechatronics Course | DP-4・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Architecture and Architectural Engineering Course | DP-4a・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Environmental Systems and Urban Planning Course | DP-4b・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Bioscience Course | DP-4a・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Biomedical Engineering Course | DP-4b・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Sports Engineering Course | DP-4c・3 | 専門分野と他分野を関連付ける素養 主軸となる分野の専門知識を他分野と関連付ける分野横断型の知識と行動力を修得し、社会で活用できる。 |
| Mathematical Sciences Course | DP-4・1 | 研究者・技術者としての基礎的素養 代数、幾何、解析、応用数学、情報数学などの専門知識を修得し、課題を抽象化して数理的な問題設定をすると共に、適切な理論的考察・計算的手法を用いて問題解決策を構築できる。 |
| Mathematical Sciences Course | DP-4・2 | キャリアを見据えた高度な専門知識 現象の背後にある数理構造やデータのパターンを理論的に解析し、社会や自然科学、工学における複雑な課題に対して数理的視点から解決戦略を提案できる。 |
The purpose of this class is to understand the necessity of numerical computation and numerical analysis in scientific and
engineering computing, to gain an understanding of numerical representation and errors in computers, as well as the underlying
principles and derivations of algorithms for solving nonlinear equations, systems of linear equations, and numerical integration.
Furthermore, students will implement various algorithms on a computer, conduct numerical experiments, and acquire the ability
to perform appropriate evaluations of the results.
Computation on computers is an indispensable technology in modern research and development. In recent years, computer performance
has improved dramatically, and various software tools running on these systems have become increasingly sophisticated, making
it easy to perform a wide range of calculations and simulations. However, if such tools are used as black-boxes without sufficient
understanding of their underlying principles, they may sometimes be misused, leading to serious errors.
This course begins with fundamental topics in numerical analysis, including the representation of numbers on computers and the nature of computational errors. It then covers the basic theory and algorithmic principles for solving nonlinear equations, systems of linear equations, and numerical integration. Furthermore, students will implement algorithms and conduct numerical experiments on specific problems in order to develop the ability to evaluate computational results appropriately and correctly. The course is primarily lecture-based, although computer-based exercises may also be included.
This course begins with fundamental topics in numerical analysis, including the representation of numbers on computers and the nature of computational errors. It then covers the basic theory and algorithmic principles for solving nonlinear equations, systems of linear equations, and numerical integration. Furthermore, students will implement algorithms and conduct numerical experiments on specific problems in order to develop the ability to evaluate computational results appropriately and correctly. The course is primarily lecture-based, although computer-based exercises may also be included.
- Understand the representation of numerical values in computers and the errors that arise from them and their propagation.
- Understand the principles of numerical methods for solving nonlinear equations, and be able to implement them on a computer and solve them numerically.
- Understand the principles of numerical methods for solving linear equations, and be able to implement them on a computer and solve them numerically.
- Understand the principles of numerical integrations, and be able to implement them on a computer and solve them numerically.
| final exam | report | Total. | |
|---|---|---|---|
| 1. | 10% | 10% | 20% |
| 2. | 20% | 10% | 30% |
| 3. | 20% | 10% | 30% |
| 4. | 10% | 10% | 20% |
| Total. | 60% | 40% | - |
Students will be evaluated comprehensively based on the final examination as well as assignments and reports.
A passing grade will be awarded if it is judged that the student has achieved approximately 60% proficiency in understanding the derivation of each numerical method, implementing the algorithms, conducting numerical experiments, and appropriately evaluating the obtained results.
A passing grade will be awarded if it is judged that the student has achieved approximately 60% proficiency in understanding the derivation of each numerical method, implementing the algorithms, conducting numerical experiments, and appropriately evaluating the obtained results.
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction: What is numerical analysis and simulation | Review of the syllabus, representation of floating point numbers, and binary numbers | 190minutes |
| 2. | Floating point numbers and numerical errors | Review of previous class | 190minutes |
| 3. | Solving nonlinear equations 1: Bisection method, False position method | Review of previous class | 190minutes |
| 4. | Solving nonlinear equations 2: Newton method, Secant method | Review of previous class | 190minutes |
| 5. | PC exercises: MATLAB programming | Review of previous class | 190minutes |
| 6. | Solving nonlinear equations 3: PC exercises | Review of previous clas | 190minutes |
| 7. | Linear equations 1: Gauss elimination method, Gaussian elimination with partial pivoting | Review of previous class | 190minutes |
| 8. | Linear equations 2: LU decomposition and norm | Review of previous class | 190minutes |
| 9. | Linear equations 3: Stationary iterative methods, Jacobi method, Gauss-Seidel method, SOR method | Review of previous class | 190minutes |
| 10. | Linear equations 4: PC exercises | Review of previous class, Matlab, report writing | 190minutes |
| 11. | Numerical integration 1: Midpoint rule, trapezoidal rule, Simpson’s rule | Review of previous class | 190minutes |
| 12. | Numerical Integration 2: Error analysis | Review of previous class | 190minutes |
| 13. | Numerical Integration 3: PC exercise | Review of previous class | 190minutes |
| 14. | Final examination and review | Review of previous class | 190minutes |
| Total. | - | - | 2660minutes |
| ways of feedback | specific contents about "Other" |
|---|---|
| Feedback in the class |
The textbook will not be specified. Materials will be distributed as necessary.
- Thu. 15:00--16:40
If you have any questions or would like to schedule a consultation, it is preferable to make an appointment in advance by email.
- Course that cultivates an ability for utilizing knowledge
- Course that cultivates a basic problem-solving skills
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | 該当しない |
Last modified : Mon Mar 23 04:07:13 JST 2026