ME 3255 Spring 2017
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.
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 (email@example.com)
Office hours: Fridays 10am-12pm in Engineering II room 315
- Graduate: TBD
- Office hours: 2 hours / week in office TBD
Prerequisite: CE 3110, MATH 2410Q
Textbook: Chapra, Steven, Applied Numerical Methods with MATLAB for Engineers and Scientists 3rd edition.
|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 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.
- 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)
|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|
|4||2/7||6||Root Finding con’d|
|5||2/14||Intro to Linear Algebra|
|6||2/21||9||Linear systems: Gauss elimination|
|2/23||10||Linear Systems: LU factorization|
|8||3/7||11||Linear Systems: Error analysis|
|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|
|12||4/4||18||Splines and Piecewise Interpolation|
|4/6||19||Numerical Integration Formulas|
|14||4/11||20||Numerical Integration of Functions|
|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!|