Computational Physics I - PHYS 3500K

Introduction

This book is constructed from the repository dedicated to Computational Physics I (PHYS 3500K) at Kennesaw State University.

Here, you will find examples and notes pertaining to the course, as well as additional material.

Note that the repository is meant to evolve during the semester. Solutions to the homework problems are given to the students taking the course, and they can be provided from the author upon request.

• 1. Making Computers Obey
• 2. Computer Number Representations
• 3. Deterministic Randomness
• 4. Random Sequences
• 5. Random Walks
• 6. Numerical Differentiation
• 7. Error Assessment in Numerical Differentiation
• 8. Numerical Integration
• 9. Minor Digression: Python functools and partial functions
• 10. Monte Carlo Methods
• 11. Matrix Computing, Trial-and-Error Searching and Data Fitting
• 12. Ordinary Differential Equations
• 13. An Introduction to Nonlinear Dynamics and Chaos
• 14. Boundary Value and Eigenvalue Problems
• 15. Partial Differential Equations
• 16. More Monte Carlo: The Metropolis Algorithm

About the Author

Andreas Papaefstathiou is Assistant Professor of Physics at Kennesaw State University. This website was originally created in January 2024 and is updated on a best-effort basis.

References

• Computational Physics, Problem Solving with Python - Rubin H. Landau, Manuel J. Páez, Christian C. Bordeianu.

• Computational Physics (Fortran Version) - Steve E. Koonin, Dawn C. Meredith.

• Nonlinear Dynamics and Chaos - Steven H. Strogatz.