PHYS 305

Course Number & Title: PHYS 305—001 Computational Physics
Course Website: https://ua-2025q1-phys305.github.io

Full Syllabus: Syllabus

Time: Tuesday & Thursday 12:30–1:45pm
Location: Social Sciences, Room 206

Instructor: Chi-kwan “CK” Chan chanc@arizona.edu Office hour: Mon 1-2pm, Wed 11am–noon (SO N332)
Teaching Assistant: Devendran “Deva” Vemula devendranvemula@arizona.edu Consultation hour: Wed 12 - 1pm (PAS 372)
Preceptor: Nikhil Garuda nikhilgaruda@arizona.edu
Please include “PHYS305” in subjects of emails

#	Week	Tuesday	Thursday
1	Jan 12–Jan 18		Overview (lab)
2	Jan 19–Jan 25	Data Representation (lab)	Numerical Linear Algebra (lab, sol)
3	Jan 26–Feb 1	Fourier Transform and Spectral Analyses (lab, sol)	Guest lecture: Useful Tools (homework 1)
4	Feb 2–Feb 8	Interpolation and Extrapolation (lab, sol)	Numerical and Automatic Derivatives (lab, sol)
5	Feb 9–Feb 15	Numerical Integration of Functions (lab, sol)	Root Finding (lab, sol)
6	Feb 16–Feb 22	Optimization Methods (lab, sol; homework)	Data Modeling I: Probability and Statistics (lab, sol)
7	Feb 24–Mar 1	Data Modeling II: Bayesian Statistics (lab, sol)	Selected techniques in machine learning (lab)
8	Mar 2–Mar 8	Project I presentations	Project I presentations (homework`)
9	Mar 9–Mar 15	Spring recess (no class)	Spring recess (no class)
10	Mar 16–Mar 22	ODE Integrators I: Explicit Methods (lab, sol)	ODE integrators II: implicit and symplectic methods (lab, sol)
11	Mar 24–Mar 29	ODE Integrators III: Advanced Topics (lab, sol)	The C programming language (homework)
12	Mar 30–Apr 5	Monte Carlo Methods I: Random Numbers and Random Walk (lab, )	Monte Carlo Methods II: Ising Model and Hopfield Network
13	Apr 6–Apr 12	Monte Carlo Methods III: Parameter Estimation and Markov Chain	Parallel Computing (homework)
14	Apr 13–Apr 19	Numerical Partial Differential Equation I: Introduction	Numerical Partial Differential Equation II: Stability Analysis
15	Apr 20–Apr 26	Numerical Partial Differential Equation III: Finite Volume	Numerical Partial Differential Equation IV: Spectral Methods (homework)
16	Apr 27–May 3	Project II presentations	Project II presentations
17	May 4–May 10	Visiting UA HPC	Reading Day (no class)

Syllabus

• Syllabus

Overview

• Overview
• Git, Jupyter, and Python

Data Representation

• Data Representation
• Errors

Numerical Linear Algebra

• Numerical Linear Algebra
• Eigenvalue Problems
• Numerical Linear Algebra Lab
• Numerical Linear Algebra Lab (Solution)

Fourier Transform and Spectral Analyses

• Fourier Transform and Spectral Analyses
• Fourier Transform and Spectral Analyses Lab
• Fourier Transform and Spectral Analyses Lab (Solution)

Useful Tools

• Useful Tools

Interpolation and Extrapolation

• Interpolation and Extrapolation
• Interpolation and Extrapolation Lab
• Interpolation and Extrapolation Lab (Solution)

Numerical and Automatic Derivatives

• Numerical and Automatic Derivatives
• Derivatives Lab
• Derivatives Lab Solution

Numerical Integration of Functions

• Numerical Integration of Functions
• Integration Lab
• Integration Lab

Root finding

• Root Finding
• Root Finding Lab
• Root Finding Lab

Optimization

• Optimization Methods
• Projectile Motion Lab
• Projectile Motion Lab

Data Modeling I

• Data Modeling I: Probability and Statistics
• Hands-On Lab: Distribution of Superluminal Velocities in Quasars
• Hands-On Lab: Distribution of Superluminal Velocities in Quasars

Data Modeling II

• Data Modeling II: Bayesian Statistics
• Bayesian Statistics Lab
• Bayesian Statistics Lab

Selected techniques in machine learning

• Machine Learning Lab: A Simple Neural Network on MNIST with JAX

ODE integrators I

• ODE Integrators I: Explicit Methods
• ODE Lab I: 4th-order Runge-Kutta Methods
• ODE Lab I: 4th-order Runge-Kutta Methods

ODE integrators II

• ODE integrators II: implicit and symplectic methods
• ODE Lab II: Central Potential Problem
• Comparing RK4 and Leapfrog
• ODE Lab II: Central Potential Problem
• Comparing RK4 and Leapfrog

ODE integrators III

• ODE Integrators III: Advanced Topics
• Computational Lagrangian Mechanics
• ODE Lab III: Advanced Topics
• ODE Lab III: Advanced Topics

The C programming language

• The C programming language

Monte Carlo I

• Monte Carlo Methods I: Random Numbers and Random Walk
• Random Number Lab I

Monte Carlo II

• Monte Carlo Methods II: Ising Model and Hopfield Network

Monte Carlo III

• Monte Carlo Methods III: Parameter Estimation and Markov Chain

Parallel Computing

• Parallel Computing

PDE I

• Numerical Partial Differential Equation I: Introduction

PDE II

• Numerical Partial Differential Equation II: Stability Analysis

PDE III

• Numerical Partial Differential Equation III: Finite Volume

PDE IV

• Numerical Partial Differential Equation IV: Spectral Methods