Mathematical Modeling MATH 345

 

Instructor Lev V. Idels

Vancouver Island University

Department of Mathematics

 

Fall 2011

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 An introduction to the application of mathematics as a tool for studying complex systems in science and engineering. Topics include model fitting, experimental modeling, modeling with difference and ordinary differential equations, and optimization. (3:0:0)

Prerequisite: Min. "B" in each of MATH 100 ( or MATH 121) and MATH 101 (or MATH 122) and min. "C" in MATH 141.

Course Objective:

This course is designed to provide the students with a fundamental understanding of how Mathematics is applied as a tool to aid in studying complex systems in science and engineering. It will help a student to overcome the difficult transition from basic assumptions made on a real problem to the setting up of a model in mathematical terms. We will introduce modern technology such as Maple and/or Matlab. When you have gone through the course, you will have learned how to tackle real-world problems.

Instructor Dr. Lev V. Idels

Office: Bld. 360/304

Course webpage: http://web.viu.ca/math/lev.html

E-mail: lev.idelsl@viu.ca

Office hours: Mon 11:40-2:00, Wed 11:40-2:00 or if you need to see me outside of the announced office hours, please set up an appointment with me, either by speaking to me before or after class, or by sending me an email message (include "MATH 345" in the subject line, please). To help me manage my email inbox, please include "MATH 345" in the subject line of any email message you send to me (without it, your message runs the risk of being deleted without being read).

Text: A First Course in Mathematical Modeling, Fourth Edition, by F.R. Giordano, M.D. Weir, and W.P. Fox

Syllabus (Tentative):

Chapters 1-2

Modeling of chemical, biological and physical systems; Dynamical systems via difference equations

Chapter 11

Modeling with differential equations.

Chapter 12

Modeling with systems of differential equations

Chapters 3-4

Fitting models to data graphically and analytically. Experimental modeling. Cubic splines and interpolation.

Chapter 7

Discrete optimization modeling.

Chapter 13

Classical optimization methods: Golden Mean, Conjugate Gradients, Constrained Optimization.

 

Grading:

Final Exam

50%

Midterm Test

20%

Five Assignments with Maple

30%

Important Dates:

1.      Assignments (TBA) No late Assignments will be accepted.

2.      Midterm Test (TBA)

3.      Final Exam (December 2011)

 



This Maple-butterfly was created by Professor Mohammad Siddique

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