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Computational Mechanics

ME 3255 Fall 2017

Public (ipynb rendering)

Github page:

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.


  • 001 MoWeFr 10:10-11:00 am, Engr II room 202 Philip E. Austin bdg (Aust) rm 434

  • 002 TuTh 12:30-1:45 PM, Engr II room 202 Charles Lewis Beach Hall (BCH) rm 317

Instructor: Prof. Ryan C. Cooper (

Office hours: TuTh 1:00-3:00pm in Engineering II room 314

Teaching Assistants:

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, Github Desktop.

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


Item Percent Requirement
Homework 40 % Turn in homeworks by assigned due date
Midterm Exam 20 % One midterm exam
Final Project 30 % A final project that will consist of code and documentation
Participation 10 % Online video-quizzes and Google Forms

Note on Homework and online forms

The Homeworks are graded based upon effort, correctness, and completeness. The forms and video-quizzes are graded based upon completion, if they are completed you get credit. It is your responsibility to make sure your answers are correct. Use the homeworks, videos, and forms as a study guide for the midterm and final project. In general, I will not post homework solutions, but I will review solutions in class.

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 8/28 1 Introduction to Numerical Methods and Github
4 Intro con’d and Roundoff/Truncation Errors
2 9/4 2 Intro to Matlab/Octave
3 Intro to m-files
3 9/11 Consistent Coding habits
Using Github and writing functions
4 9/18 5 Root Finding
6 Root Finding con’d
5 9/25 7 Optimization
8 Linear Algebra
6 10/2 9 Linear systems: Gauss elimination
10 Linear Systems: LU factorization
7 10/9 10 Linear Systems: Cholesky factorization
1-10 Review
8 10/16 1-10 Midterm Review
1-10 Midterm
9 10/23 12 Eigenvalues
11 Linear Systems: Error analysis
10 10/30 14 Statistics and Monte Carlo models
15 Curve fitting: least squares and nonlinear regression
11 11/6 18 Splines and Piecewise Interpolation
19 Numerical Integration Formulas
12 11/13 20 Numerical Integration of Functions
21 Numerical Differentiation
14 11/20 Thanksgiving break
Thanksgiving break
15 11/28 22 ODEs: Initial value problem
23 ODEs: Adaptive methods and stiff systems
16 12/4 24 ODEs: Boundary value problems
Wrap up and final project discussions
17 12/11 Finals Finals Best of Lucks!


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