| Academic Year |
2024Year |
School/Graduate School |
Graduate School of Integrated Sciences for Life (Master's Course) Division of Integrated Sciences for Life Program of Mathematical and Life Sciences |
| Lecture Code |
WG112001 |
Subject Classification |
Specialized Education |
| Subject Name |
計算数理科学Ａ |
Subject Name
（Katakana） |
ケイサンスウリカガクエー |
Subject Name in
English |
Computational Mathematics A |
| Instructor |
FUJII MASASHI |
Instructor
(Katakana) |
フジイ マサシ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Thur1-4：Online |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
Mathematical modeling of life sciences, differential equations, stochastic processes |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
Several mathematical modeling methods will be introduced and students make some programs. |
| Class Schedule |
Session 1: Guidance and review
Session 2: Role of header files, compilation, and Makefile+α
Session 3: How to use random numbers and speed comparison
Session 4: Stochastic chemical reactions (Gillespie method and τ-leap method)
Session 5: Various solutions and errors of ordinary differential equations
Session 6: Optimization (stochastic optimization and gradient method) |
Text/Reference
Books,etc. |
distribute as necessary |
PC or AV used in
Class,etc. |
|
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
This will be a practical class in which students will implement their own algorithms, so please be proactive and ask questions even outside of class time. |
| Requirements |
Any type of programming language is acceptable, but it is preferable to have some experience with numerical computation before the course. |
| Grading Method |
Evaluated by reports |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |