2019　Mathematical Modeling

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Undergraduate major in Mathematical and Computing Science
Instructor(s)
Takayasu Misako  Takayasu Hideki
Class Format
Lecture
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(W834)  Thr7-8(W834)
Group
-
Course number
MCS.T315
Credits
2
2019
Offered quarter
3Q
Syllabus updated
2019/8/2
Lecture notes updated
2019/11/21
Language used
Japanese
Access Index

Course description and aims

For understanding uncertain and/or complex phenomena that are difficult to describe from the first principle, it is particularly important to appropriately formulate them as mathematical problems. This task is often termed "modeling". This course shows elementary mathematical techniques that are necessary for the modeling, illustrating representative probabilistic and/or nonlinear phenomena.

Student learning outcomes

The goal is to acquire basic skills for engaging in advanced modeling of more complex phenomena by learning elementary mathematical models for probabilistic and/or nolinear dynamical phenomena.

Keywords

Random variable, probability distribution, correlation, diffusion phenomena, Brownian motion, branching process, phase transition, transport phenomena, complex network

Competencies that will be developed

 ✔ Specialist skills Intercultural skills Communication skills ✔ Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

For each topic, we first show concrete target phenomena, and introduce how they are formulated as mathematical problems.

Course schedule/Required learning

Course schedule Required learning
Class 1 Introduction to modeling General introduction to observation, modeling, analysis and evaluation
Class 2 Observation of phenomena and basic models 1 Basic distributions such as exponential distribution and the normal distributions and corresponding mathematical models
Class 3 Observation of phenomena and basic models 2 Power law distributions and corresponding mathematical models
Class 4 Observation of phenomena and basic models 3 Nonlinear dynamics and corresponding mathematical models
Class 5 Modeling of diffusion phenomena 1 Macroscopic irreversibility of diffusion phenomenon, and derivation of diffusion equation
Class 6 Modeling of diffusion phenomena 2 Microscopic view of diffusion phenomenon and Brownian motion
Class 7 Modeling of diffusion phenomena 3 Models of price fluctuations in financial markets are introduced as an application of diffusion
Class 8 Modeling of diffusion phenomena 4 Microscopic agent-based models of price fluctuations in financial markets are introduced as an application of diffusion
Class 9 Modeling of branching and aggregation phenomena 1 Branching process and its modeling
Class 10 Modeling of branching and aggregation phenomena2 Aggregation process and its modeling
Class 11 Modeling of phase transition phenomena 1 Basic models of phase transition, basic properties and theoretical solutions
Class 12 Modeling of phase transition phenomena 2 Transport phenomena and congestion phase transition
Class 13 Modeling of phase transition phenomena 3 Self-organized criticality and related models
Class 14 Modeling of complex networks 1 Complex Networks, Adjacency matrix, characterization of networks
Class 15 Modeling of complex networks 2 Complex networks of business firms: modeling and simulation

None.

Reference books, course materials, etc.

To be distributed electronically when needed.

Assessment criteria and methods

Students' understanding will be assessed by final exam.

Related courses

• MCS.T211 ： Applied Calculus
• MCS.T203 ： Linear Algebra and Its Applications
• MCS.T223 ： Mathematical Statistics
• MCS.T212 ： Fundamentals of Probability

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Basic knowledge and skills about linear algebra, calculus, probability theory, and statistics are required.