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2024 SEMINAR INFORMATION

Lectures will be held weekly, with the same lecture taught once on a weekday and once during the weekend

Lectures will last 2 hours typically

DATES

Jan 22nd through May 26th, 2024

DAYS & TIMES

Wednesdays 4pm-6pm PT / 7pm-9pm ET

Saturdays 10am-12pm PT/ 1pm-3pm ET

REGISTRATION DEADLINE

Apply by December 8th, 2023
to be a 2024 CRANE scholar

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2024 SCHEDULE

Part I: Introduction to Python

Jan 22nd to Feb 16th, 2024

Learn Python basics in preparation for Part II including variables, loops, and functions. Each class will be taught twice.

Part II: Numerical Methods

Feb 20th to March 22nd, 2024

Learn how to build basic physics simulations from scratch, using numerical integration, finite difference methods, etc.

Part III: Supplementary Skills (Optional)

March 25th to April 5th, 2024

Learn practical skills for a scientific workflow including LaTeX, using the Terminal, and Git. This time can also be used as a break for students who would rather have the time off.

Part IV: Advanced Algorithms

April 8th to May 26th, 2024

Advanced topics including signal and image processing, particle-in-cell codes, astronomy data analysis and Monte Carlo simulations will be taught during parallel multi-week seminar sessions

SEMINAR SYLLABUS

Part I
 
Introduction to Python

January 22nd to February 16th, 2024

Week 1
Introduction to Python I: Syntax, Variables, and Arrays
Week 2
Introduction to Python II: Loops, Functions, and Plotting
Week 3
Introduction to Python III: Data Analysis and Visualization
Week 4
Review session and mini project
Part II
 
Numerical Methods

February 20th to March 22nd, 2024

Week 5
Numerical Differentiation and Discretization: Euler's Method

Solve & evolve basic mechanics problems with Euler's method

Week 6
Numerical Differentiation and Discretization: Runge-Kutta Method

Solve & evolve the same mechanical systems as last week with a new method

Week 7
Solving Complex Physics Problems with Built-in Python Solvers

Use Python's Runge-Kutta-based solvers to launch a rocket and evolve a planetary system with Kepler's laws

Week 8
1D Finite Difference Method

Solving Poisson's Equation in 1D: Electrostatics, Diffusion, and Heat Transfer

Week 9
The Fast Fourier Transform (FFT)

Doing Fourier transforms of 1D and 2D data, how to filter signals with FFT spectra

Part III (Optional)
 
Supplemental Skills

March 25th to April 5th, 2024

LaTeX


LaTeX is a software for preparing nicely formatted documents, typically scientific papers, resumes or presentations. Learn the basics of this extremely useful tool for making extremely nicely formatted papers, typesetting equations and vastly simplifying making bibliographies.

Terminal


The Terminal is a prompt where commands can be entered to navigate around one's operating system and perform many tasks. Learn basics of Bash, the language of the terminal, and how to feel like a hacker in the movies by remotely logging in to a computer.

Git


Git is a system of managing files and code that allows users to track versions of each file. Learn about managing a coding project with the basic commands of Git.

Part IV
 
Advanced Algorithms

April 8th to May 26th, 2024

  

Advanced algorithms will be taught in seminar series of up to 5 weeks, with some tracks running in parallel and some staggered to allow students to participate in multiple tracks.

Monte Carlo (MC) Track


Probability/Cumulative Distribution Functions

Random Walks and Markov Chains

Solving the Neutron Transport Equation

Particle in Cell (PiC) Track


 Boris Push Algorithm

3D Finite Difference Electromagnetic Equations

PIC Methods in Plasma Physics

SoftwareL SMILEI and ZPIC

Astronomy Data Analysis (ADA) Track

APIs, Advanced Plotting, and an Astronomer’s Toolbox!

Introduction to Machine Learning for Astronomers

Nuts & Bolts of Astronomical Observations: Photometry + Spectroscopy

Signal and Image Processing (SIP) Track

Feature tracking + image alignment

Interferometry analysis

Langmuir probe analysis

Molecular Dynamics

Coupled Motion

Multi-body systems

Ensembles, statistics, and measured properties

Machine Learning

Regression

Gradient Descent

Neural Networks

Magnetohydrodynamics with FLASH

 

Sod test problem & initialization

Oblique planar shocks & boundary conditions

Laser-target experiments

MHD shock and current sheet

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