An Introduction to Scientific Computing

Introduction

This book is derived from material constructed for the purposes of the “Computational Physics I (PHYS 3500K)” course at Kennesaw State University, as well as introductory material for research in particle physics phenomenology.

Here, you will find examples and notes, as well as additional material.

• 1. Talking to Computers: Turning Ideas into Instructions
• 2. Computer Number Representations
• 3. Randomness and Random Walks
• 4. Numerical Differentiation and Integration
• 5. Monte Carlo Methods
• 6. Matrix Computing, Trial-and-Error Searching and Data Fitting
• 7. Ordinary Differential Equations
• 8. An Introduction to Nonlinear Dynamics and Chaos
• 9. Boundary Value and Eigenvalue Problems
• 10. Partial Differential Equations
• 11. More Monte Carlo: The Metropolis Algorithm
• 12. An Introduction to Particle Physics Phenomenology
• 13. A Brief Introduction to Monte Carlo Event Generators in Particle Physics
• 14. Parton Shower Monte Carlos
• 15. Artificial Intelligence and Machine Learning in Physics

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.

• Modern Particle Physics - Mark Thomson.

• How-to: write a parton-level Monte Carlo particle physics event generator, arXiv:1412.4677.

• Pyresias: How To Write a Toy Parton Shower, Andreas Papaefstathiou, arXiv:2406.03528.