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2027 SEMINAR INFORMATION

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

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Lectures will last 2 hours typically

DATES

January 27th through May 9th, 2027*

LECTURE DAYS & TIMES

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

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

REGISTRATION DEADLINE

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Apply by November 29th, 2026
to be a 2027 CRANE scholar

*This schedule is tentative and could change slightly

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2027 SCHEDULE

Part I: Introduction to Python

January 27th to February 21st, 2027

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

Part II: Numerical Methods

February 24th to March 28th, 2027

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

Part III: Advanced Algorithms

April 3rd to May 9th, 2027

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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 27th to February 21st, 2027

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 24th to March 28th, 2027

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
 
Advanced Algorithms

April 3rd to May 9th, 2027

  

Advanced algorithms will be taught in seminar series of up to 5 weeks, with most tracks running in parallel.

Monte Carlo (MC) Track
Dates TBD

  1. Exploring randomization through mini-projects to grasp concepts of Monte Carlo

  2. Solving an actual math problem using Monte Carlo

  3. Applying Monte Carlo to neutronics of a barebones nuclear fission reactor

  4. Optional buffer week to help complete unfinished projects from Weeks 1-3

Signal and Image Processing (SIP) Track
Dates TBD

  1. Analysis and Model-Fitting of Langmuir Probe Data

  2. Advanced signal filtering Techniques

  3. Model-fitting for plasma density for Laser Interferometry Data

  4. Convolutions and their many applications

Magnetohydrodynamics with FLASH
Dates TBD

  1. What is FLASH, how to get it, and how to use it

  2. The Sedov and Sod shock tube problems and how to make simple changes to the FLASH code

  3. The double mach reflection problem and how to create boundary conditions

  4. Magnetic reconnection and how to define parameters 

  5. Laser-slab test problem

Astronomy Data Analysis (ADA) Track
Dates TBD

  1. Basic Queries with Astronomical Data Query Language

  2. Coordinate Transformations Using Astropy

  3. Advanced Plotting using Astronomical Data Part 1

  4. Advanced Plotting using Astronomical Data Part 2

Particle in Cell (PiC) Track
Dates TBD

  1. Introduction to Julia and Euler's and Boris Push Method to solve Newton's Equations for a charged particle (cyclotron motion)

  2. 1D & 2D  Finite Difference Method for Electrostatics and Magnetostatics

  3. Particle-in-Cell Algorithm

  4. Putting it all together (Penning Cell, Magnetic Mirror)

Open Science and Research Software Engineering Track
Dates TBD

  1. Open science, knowledge access, and psychological safety

  2. Contributing to an open source project with Git and GitHub

  3. Creating Python scripts and packages with uv

  4. Software testing with pytest

Machine Learning
Dates TBD

  1. Linear and Logistic Regression

  2. Gradient Descent

  3. Neural Networks

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