From 5f606a4959fdb789fa29e5a9149c41545559893c Mon Sep 17 00:00:00 2001 From: "Ryan C. Cooper" Date: Mon, 20 Jan 2020 13:32:16 -0500 Subject: [PATCH] added syllabus --- README.html | 56 +++++++++++ syllabus.html | 273 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 329 insertions(+) create mode 100644 README.html create mode 100644 syllabus.html diff --git a/README.html b/README.html new file mode 100644 index 0000000..5d28c0e --- /dev/null +++ b/README.html @@ -0,0 +1,56 @@ + + + + + + + README + + + + +

Computational Mechanics

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Welcome to Computational Mechanics.

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This project is a collection of learning modules in engineering computations for undergraduate students. These materials are a combination of work from Prof. Ryan C. Cooper at the University of Connecticut Mechanical Engineering Department and the Engineering Computations Modules from Prof. Lorena A. Barba and doctoral student Natalia C. Clement at the George Washington University, Mechanical and Aerospace Engineering Department.

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Each learning modules is made up of three or four lesson, written as a Jupyter notebooks. We address an area of application or skills in computing in each notebook and each module has an overall objective. We use Python as the programming language.

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The overall goal of the course is learn to frame engineering problems as computational methods. Once we can communicate our engineering problems to Python code (or any other computer language) we can use standardized computational methods to solve those problems.

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CompMech01-Getting Started

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CompMech02-Analyze-data

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CompMech03- Initial Value Problems

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CompMech04- Linear Algebra

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CompMech05- Boundary Value Problems

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License

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All content is under Creative Commons Attribution CC-BY 4.0, and all code is under BSD-3 clause. We are happy if you re-use the content in any way!

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License License: CC BY 4.0

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Computational Mechanics

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ME 3255 Spring 2020

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Github page: https://github.uconn.edu/rcc02007/Computational_Mechanics/

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JupyterHub server: https://compmech.uconn.edu/

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Course Description

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This course introduces students to computational methods in Python. Computational methods, best programming practices, and version control are introduced. These methods will be applied to a number of physics-based problems.

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This is a project-based introduction to computational mechanics. There are five modules with exercises, homeworks, and final projects. The overall goal of the course is learn to frame engineering problems as computational methods. Once we can communicate our engineering problems to Python code (or any other computer language) we can use standardized computational methods to solve those problems.

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Course Expectations

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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, Python functions and programming best practices.

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Lectures: TTh 9:30-10:45 AM, MCHU rm 301

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Lab times: TTh 9:30-10:00 AM and 10:15-10:45 AM, EII rm 202

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Instructor: Prof. Ryan C. Cooper ()

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Office hours: MW 9:00-11:00 AM in EII rm 314

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Teaching Assistants:

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Course Information

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Prerequisite: CE 3110, MATH 2410Q

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Tools used: Python, Jupyter , git, and Github

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Required Resources:

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Minimum Technical Skills:

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Recommended Resources:

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Recommended Textbooks:

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Grading

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ItemPercentRequirement
Participation30 %Complete the notebook exercises and discussion questions
Homework30 %Complete the end-of-notebook problems
Projects40 %Complete the module project and submit to Github
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Note on Participation

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You will have 30 minutes twice a week to work in the computer lab with me. I expect you to come to the lab with exercises and discussions complete. For the exercises/discussions that you have questions, I expect you to prepare some questions to help me help you understand the material. You will submit a pdf of your completed notebook at the end of the lab session time.

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Note on Homeworks

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The homework assignments are the problem sets at the end of each notebook. You do not have to complete these during the lab session time. They will be due before the next lab.

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Academic Integrity:

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

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DateSubject/NotebookModule
Tue (01-21)Welcome!Lecture
Thu (01-23)01_Interacting_with_PythonGetting-Started
Tue (01-28)02_Working_with_PythonGetting-Started
Thu (01-30)03_Numerical_errorGetting-Started
Tue (02-04)Module 1 ProjectReview and submit
Thu (02-06)meet MCHU 301-ReviewLecture
Tue (02-11)01_Cheers_Stats_BeersAnalyze Data
Thu (02-13)02_Seeing_StatsAnalyze Data
Tue (02-18)03_Linear_Regression_with_Real_DataAnalyze Data
Thu (02-20)04_Stats_and_MontecarloAnalyze Data
Tue (02-25)module 2 ProjectReview and submit
Thu (02-27)01_Catch_MotionInitial Value Problems
Tue (03-03)02_Step_FutureInitial Value Problems
Thu (03-05)03_Get_OscillationsInitial Value Problems
Tue (03-10)04_Shooting_solutionsInitial Value Problems
Thu (03-12)Module 3 ProjectReview and submit
Tue (03-17)Spring Break!!R&R
Thu (03-19)Spring Break!!R&R
Tue (03-24)meet MCHU 301-ReviewLecture
Thu (03-26)01_Solving_equationsLinear Algebra
Tue (03-31)02_Gauss_eliminationLinear Algebra
Thu (04-02)03_Linear_regression_revisitedLinear Algebra
Tue (04-07)Catch-up dayLinear Algebra
Thu (04-09)Module 4 projectReview and Submit
Tue (04-14)01_Finite_differencesBoundary Value Problems
Thu (04-16)02_Solutions_in_1DBoundary Value Problems
Tue (04-21)03_Solutions_in_2DBoundary Value Problems
Thu (04-23)Catch-up dayBoundary Value Problems
Tue (04-28)Review course materialLecture
Thu (04-30)Module 5 projectReview and Submit
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