Academic Notes
Scientific Computing Learning Map
Jun 2026
A living map for connecting numerical methods, computational physics, modelling projects, and reproducible code.
- scientific computing
- learning notes
- reproducibility
Purpose
This note organizes self-study across numerical methods, computational physics, modelling projects, and reproducible code. It is designed to grow into a map of what has been learned, implemented, and demonstrated through projects.
Core threads
- Numerical ODE and PDE methods
- Monte Carlo methods and uncertainty
- Scientific visualization
- Reproducible reports and notebooks
- Model validation and sensitivity analysis
How to keep it useful
The map is most useful when each topic links to a project, a note, or a small code example. Topic lists should be backed by evidence of use rather than acting as a generic syllabus.