Computational_physics
Here is a blog post about the Computational_physics project:
Solving Physics with Python: A Look at a Computational Toolkit
Physics is no longer just about chalkboards and expensive lab equipment. A “third pillar” of modern science has emerged: computational physics. This is the art of using code and numerical methods to simulate and solve problems that are too complex for pen and paper.
A fantastic example of this in practice is the Computational_physics repository by Chris Sunny. This project, structured as a series of assignments, serves as a practical, hands-on journey through the core techniques of the field.
A Code-Based Physics Course
The repository is neatly organized into five assignments (assignment1 through assignment5) plus an examples folder. This structure strongly suggests it’s the coursework from a university-level class, which is great for learners as it provides a logical progression of topics, from basic numerical methods to more complex simulations.
The entire repository is 100% Python. This is a smart choice, as Python—powered by its “scientific stack” of libraries like NumPy, SciPy, and Matplotlib—has become the language of choice for scientific computing. It’s readable, powerful, and has a vast ecosystem of tools for everything from matrix math to plotting.
What’s Inside?
While we can’t see the exact problem descriptions, a computational physics curriculum typically involves a standard set of powerful techniques. Looking at the structure, this repository is likely a practical playbook for solving problems like:
- Solving Differential Equations: Simulating planetary orbits using methods like Runge-Kutta, or modeling a pendulum’s swing.
- Partial Differential Equations: Modeling the flow of heat through a metal plate (the Heat Equation) or the vibration of a string (the Wave Equation) using techniques like the finite-difference method.
- Stochastic Methods: Using randomness to find solutions, such as with Monte Carlo methods to model a magnetic system (the Ising model) or simulate radioactive decay.
- Matrix & Linear Algebra: Solving problems in quantum mechanics where the properties of a system are represented by large matrices.
Why You Should Check It Out
This repository is an excellent resource for a few key audiences:
- Physics Students: If you’re currently taking (or about to take) a computational physics course, this is a fantastic reference. You can see how problems are broken down and implemented in clean, modern Python.
- Programmers: If you’re a developer curious about scientific applications, this is a perfect window into the field. You can see how core programming concepts are applied to solve complex, real-world physics problems.
- Self-Learners: It serves as a ready-made curriculum. You can challenge yourself to solve the same (inferred) problems and compare your solutions.
This repository is a perfect example of learning by doing. It bridges the gap between abstract physical theory and practical, runnable code.
Check out the Computational_physics repository on GitHub here!