K6713020 Simulation Engineering

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Engineering

Lecture Code K6713020 Subject Classification Specialized Education

Subject Name シミュレーション工学

Subject Name
（Katakana）シミュレーションコウガク

Subject Name in
English Simulation Engineering

Instructor AMBE YUICHI

Instructor
(Katakana) アンベ　ユウイチ

Campus Higashi-Hiroshima Semester/Term 2nd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Weds1-4：ENG 107

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 11 : Electrical, Systems, and Control Engineering

Eligible Students

Keywords Euler Method, Runge-Kutta Method, Newton's Method, Linearization, Eigenvalues, Simulation, Python

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) Program of Electrical,Systems and Information Engineering
（Abilities and Skills）
・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering.

Class Objectives
/Class Outline As target systems become larger and more complex, the need for simulation—one of the experimental methods alongside various analytical approaches—is increasing. Using the visually intuitive motion of a pendulum (solving time-evolving nonlinear differential equations) as a primary example, this course covers the modeling, simulation, and analysis methods of dynamic systems.
Especially today, when simulations can be easily executed using AI, it is crucial to avoid treating them as "black boxes." Instead, engineers must understand the underlying principles and limitations to accurately evaluate simulation results. This course emphasizes practical training to develop these essential skills.

Upon successful completion of this course, students will be able to:
1. Solve ordinary differential equations (ODEs) numerically.
2. Explain the relationship between step size and accuracy in numerical computations.
3. Evaluate the validity of time-evolution simulations.
4. Discuss simulation results using state space (phase plane) analysis.
5. Implement linear control algorithms within a simulation environment.

Class Schedule Lesson 1: Introduction - Various Types of Simulations
Lesson 2: Introduction to Numerical Computation - Basics of Python and NumPy
Lesson 3: Numerical Methods for ODEs (1) - Euler Method
Lesson 4: Numerical Methods for ODEs (2) - Runge-Kutta Method
Lesson 5: Numerical Methods for ODEs (3) - Step Size and Accuracy
Lesson 6: Basics of Modeling - Mathematical Representation of Systems
Lesson 7: Exercise (1) - Pendulum Simulation and Phase Plane
Lesson 8: Exercise (2) - Pendulum Simulation and Validity Evaluation
Lesson 9: Stabilization of an Inverted Pendulum (Base Joint Torque Control)
Lesson 10: Linearization and Jacobian Matrix
Lesson 11: Eigenvalues and System Stability
Lesson 12: Numerical Solution of Simultaneous Equations - Finding Equilibrium Points
Lesson 13: Exercise (3) - Inverted Pendulum Control Simulation
Lesson 14: Exercise (4) - Inverted Pendulum Control Simulation and Comprehensive Review
Lesson 15: Course Summary Final Examination

Assignments will be given during the exercises in Lessons 7-8 and Lessons 13-14. A final exam will be conducted at the end of the course.

Text/Reference
Books,etc. No specific textbook is required, but the course is based on general textbooks on numerical methods. Course materials will be shared using Google Colab or similar platforms.

PC or AV used in
Class,etc. Handouts, Visual Materials, Microsoft Teams

(More Details) Powerpoint, Google Colab or similar platforms
Personal Computer

Learning techniques to be incorporated Post-class Report

Suggestions on
Preparation and
Review Lesson 1: Review the provided materials to understand the role of simulations.
Lesson 2: Practice and understand Python syntax and usage through coding.
Lesson 3: Review the Euler method by manually deriving the mathematical formulas.
Lesson 4: Review the Runge-Kutta method by manually deriving the mathematical formulas.
Lesson 5: Understand the limits of accuracy guaranteed by numerical methods for ODEs.
Lesson 6: Review the methodology of mathematical modeling.
Lessons 7-8: Through pendulum simulations, review numerical methods for ODEs and grasp implementation issues and accuracy limitations.
Lesson 9: Examine the stabilization methods for an inverted pendulum (base joint torque control) while running the program.
Lesson 10: Understand system linearization and the Jacobian matrix.
Lesson 11: Review linear algebra and differential equations to understand eigenvalues and stability using practical examples.
Lesson 12: Understand the numerical solution of simultaneous equations using Newton's method, taking the derivation of equilibrium points as an example.
Lessons 13-14: Review system stability and control engineering through the modeling and simulation of an inverted pendulum.
Lesson 15: Review everything learned throughout the course via the overall summary.

Requirements Students MUST bring a laptop PC to class, as we will use web services like Google Colab to write and execute programs during the lecture. Additionally, students MUST bring a notebook and writing utensils, as manual calculation tasks (e.g., mathematical modeling) will be assigned during class.

Grading Method Grades will be based on two submitted assignment reports (40%) and a final exam (60%). A total score of 60% or higher is required to pass. Consultation and peer learning among students are highly encouraged when working on the assignment reports (however, note that you will not be able to solve the final exam if you do not understand the content yourself). The final exam questions will be based on the contents of the assignment reports.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Students MUST bring a laptop PC to class, as we will use web services like Google Colab to write and execute programs during the lecture. Additionally, students MUST bring a notebook and writing utensils, as manual calculation tasks (e.g., mathematical modeling) will be assigned during class.

Other This class is renewed on this year

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.

Academic Year	2026Year	School/Graduate School	School of Engineering
Lecture Code	K6713020	Subject Classification	Specialized Education
Subject Name	シミュレーション工学
Subject Name （Katakana）	シミュレーションコウガク
Subject Name in English	Simulation Engineering
Instructor	AMBE YUICHI
Instructor (Katakana)	アンベ　ユウイチ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Weds1-4：ENG 107
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	11 : Electrical, Systems, and Control Engineering
Eligible Students
Keywords	Euler Method, Runge-Kutta Method, Newton's Method, Linearization, Eigenvalues, Simulation, Python
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)	Program of Electrical,Systems and Information Engineering （Abilities and Skills）・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering.
Class Objectives /Class Outline	As target systems become larger and more complex, the need for simulation—one of the experimental methods alongside various analytical approaches—is increasing. Using the visually intuitive motion of a pendulum (solving time-evolving nonlinear differential equations) as a primary example, this course covers the modeling, simulation, and analysis methods of dynamic systems. Especially today, when simulations can be easily executed using AI, it is crucial to avoid treating them as "black boxes." Instead, engineers must understand the underlying principles and limitations to accurately evaluate simulation results. This course emphasizes practical training to develop these essential skills. Upon successful completion of this course, students will be able to: 1. Solve ordinary differential equations (ODEs) numerically. 2. Explain the relationship between step size and accuracy in numerical computations. 3. Evaluate the validity of time-evolution simulations. 4. Discuss simulation results using state space (phase plane) analysis. 5. Implement linear control algorithms within a simulation environment.
Class Schedule	Lesson 1: Introduction - Various Types of Simulations Lesson 2: Introduction to Numerical Computation - Basics of Python and NumPy Lesson 3: Numerical Methods for ODEs (1) - Euler Method Lesson 4: Numerical Methods for ODEs (2) - Runge-Kutta Method Lesson 5: Numerical Methods for ODEs (3) - Step Size and Accuracy Lesson 6: Basics of Modeling - Mathematical Representation of Systems Lesson 7: Exercise (1) - Pendulum Simulation and Phase Plane Lesson 8: Exercise (2) - Pendulum Simulation and Validity Evaluation Lesson 9: Stabilization of an Inverted Pendulum (Base Joint Torque Control) Lesson 10: Linearization and Jacobian Matrix Lesson 11: Eigenvalues and System Stability Lesson 12: Numerical Solution of Simultaneous Equations - Finding Equilibrium Points Lesson 13: Exercise (3) - Inverted Pendulum Control Simulation Lesson 14: Exercise (4) - Inverted Pendulum Control Simulation and Comprehensive Review Lesson 15: Course Summary Final Examination Assignments will be given during the exercises in Lessons 7-8 and Lessons 13-14. A final exam will be conducted at the end of the course.
Text/Reference Books,etc.	No specific textbook is required, but the course is based on general textbooks on numerical methods. Course materials will be shared using Google Colab or similar platforms.
PC or AV used in Class,etc.	Handouts, Visual Materials, Microsoft Teams
(More Details)	Powerpoint, Google Colab or similar platforms Personal Computer
Learning techniques to be incorporated	Post-class Report
Suggestions on Preparation and Review	Lesson 1: Review the provided materials to understand the role of simulations. Lesson 2: Practice and understand Python syntax and usage through coding. Lesson 3: Review the Euler method by manually deriving the mathematical formulas. Lesson 4: Review the Runge-Kutta method by manually deriving the mathematical formulas. Lesson 5: Understand the limits of accuracy guaranteed by numerical methods for ODEs. Lesson 6: Review the methodology of mathematical modeling. Lessons 7-8: Through pendulum simulations, review numerical methods for ODEs and grasp implementation issues and accuracy limitations. Lesson 9: Examine the stabilization methods for an inverted pendulum (base joint torque control) while running the program. Lesson 10: Understand system linearization and the Jacobian matrix. Lesson 11: Review linear algebra and differential equations to understand eigenvalues and stability using practical examples. Lesson 12: Understand the numerical solution of simultaneous equations using Newton's method, taking the derivation of equilibrium points as an example. Lessons 13-14: Review system stability and control engineering through the modeling and simulation of an inverted pendulum. Lesson 15: Review everything learned throughout the course via the overall summary.
Requirements	Students MUST bring a laptop PC to class, as we will use web services like Google Colab to write and execute programs during the lecture. Additionally, students MUST bring a notebook and writing utensils, as manual calculation tasks (e.g., mathematical modeling) will be assigned during class.
Grading Method	Grades will be based on two submitted assignment reports (40%) and a final exam (60%). A total score of 60% or higher is required to pass. Consultation and peer learning among students are highly encouraged when working on the assignment reports (however, note that you will not be able to solve the final exam if you do not understand the content yourself). The final exam questions will be based on the contents of the assignment reports.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Students MUST bring a laptop PC to class, as we will use web services like Google Colab to write and execute programs during the lecture. Additionally, students MUST bring a notebook and writing utensils, as manual calculation tasks (e.g., mathematical modeling) will be assigned during class.
Other	This class is renewed on this year
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.

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