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Greetings!
Thanks for joining us for UConn's Computational Mechanics course. This is a hands-on, portfolio-based course. The course website is built with an open source license so after you fork the repository its yours! You can build your own website, notebooks, etc. and share them with anyone you meet. Just make sure you link back to this course website.
JupyterHub server: https://compmech.uconn.edu/
Join us on the Course GitHub Discussions.
Welcome to Computational Mechanics!
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.
Each learning module is made up of three or four lessons, written as 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.
The overall goal of the course is to frame engineering problems as computational methods. Once we can communicate our engineering problems to Python code (or any other computer language) we use standard computational methods to solve those problems.
View the current syllabus $\leftarrow$ click here
The course schedule is kept up to date on the Google Calendar here:
<iframe src="https://calendar.google.com/calendar/embed?height=400&wkst=1&bgcolor=%23ffffff&ctz=America%2FNew_York&mode=AGENDA&src=Y19jbGFzc3Jvb201MTVlZTBmZEBncm91cC5jYWxlbmRhci5nb29nbGUuY29t&color=%23202124" style="border:solid 1px #777" width="600" height="400" frameborder="0" scrolling="no"></iframe>- Getting comfortable with Python
- Quantifying error in computational methods
- Describing and plotting data
- Some statistics
- Monte Carlo modelling
- Creating functions that are physical models
- Solving ordinary differential equations
- Solving nonlinear equations
- Define sets of equations as matrix algebra
- Solve for multiple equations for multiple unknown variables
- Continue creating functions that are physical models
- Solve 1D and 2D partial differential equations with finite difference approximations
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!
