Computational Mechanics

ME 3255 Spring 2017

Github page: [https://github.uconn.edu/rcc02007/ME3255S2017.git]

Course Description

This course introduces students to scientific programming utilizing Matlab/Octave. Numerical methods, best programming practices and version control are introduced. These methods will be applied to a number of physics-based problems.

Course Expectations

Students are expected to create numerical approximations for linear and nonlinear problems, understand approximations due to floating point operations and numerical approaches and solve differential equations using numerical differentiation and integration. Students are also expected to learn basics of git version control, matlab/octave functions and programming best practices.

Lectures: TTh 9:30-10:45 AM, Francis L. Castleman bdg (CAST) room 212

Instructor: Prof. Ryan C. Cooper (ryan.c.cooper@uconn.edu)

Office hours: Mon 2:30-4:30pm and Thur 11am-1pm in Engineering II room 315

Teaching Assistants:

Graduate: Peiyu Zhang peiyu.zhang@uconn.edu
Office hours: Friday 9:00-11:00am in Engineering II room 315

Course Information

Prerequisite: CE 3110, MATH 2410Q

Textbook: Chapra, Steven, Applied Numerical Methods with MATLAB for Engineers and Scientists 3rd edition.

Tools used: Matlab, Octave , Github.

Recommended tools: Github Desktop, git, Atom (text editor), Vim (text editor), Jupiter notebook (with matlab or octave kernel)

Grading

Item	Percent	Requirement
Homework	50 %	Turn in homeworks by assigned due date
Midterm Exam	10 %	One midterm exam
Final Project	30 %	A final project that will consist of code and documentation
Participation	10 %	During class online form will be sent out, you must submit form with your user ID to get credit

Note on Homework and online forms

The Homeworks are graded based upon effort, correctness, and completeness. The forms are not graded at all, if they are completed you get credit. It is your responsibility to make sure your answers are correct. Use the homeworks and forms as a study guide for the exams. In general, I will not post homework solutions.

Academic Integrity:

The instructors of this class have a zero-tolerance policy for academic misconduct, that is copying others' work either in the lab, field, or on an exam. Any student work that is found to be in violation of the university policy regarding academic misconduct will be assigned a grade of zero at a minimum.
Read and understand The UConn Student Code of Conduct. Students will follow all University regulations concerning the final exam.

Course Schedule (which is subject to change based upon feedback and pace of course)

Week	Date	Chapter	Topic
1	1/17	1	Introduction to Numerical Methods and Github
	1/19	4	Intro con’d and Roundoff/Truncation Errors
2	1/24	2	Intro to Matlab/Octave
	1/26	3	Intro to m-files
3	1/31		Consistent Coding habits
	2/2	5	Root Finding
4	2/7	6	Root Finding con’d
	2/9	7	Snow Day
5	2/14		Optimization
	2/16	8	Linear Algebra
6	2/21	9	Linear systems: Gauss elimination
	2/23	10	Linear Systems: LU factorization
7	2/28	11	Linear Systems: Error analysis
	3/2	12	Eigenvalues
8	3/7	1-10	Midterm Review
	3/9	1-10	Midterm
9	3/14	N/A	Spring Break!
	3/16	N/A	Spring Break!
10	3/21	12	Linear Systems: Iterative methods
	3/23	14	Curve fitting: linear regression
11	3/28	15	Curve fitting: least squares and nonlinear regression
	3/30	17	Polynomial interpolation
12	4/4	18	Splines and Piecewise Interpolation
	4/6	19	Numerical Integration Formulas
14	4/11	20	Numerical Integration of Functions
	4/13	21	Numerical Differentiation
15	4/18	22	ODEs: Initial value problem
	4/20	23	ODEs: Adaptive methods and stiff systems
16	4/25	24	ODEs: Boundary value problems
	4/27		Wrap up and final project discussions
17	5/1	Finals	Finals Best of Lucks!

rcc02007/ME3255S2017

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