Education & outreach#
Workshops with JHU URSCA#
Working with JHU’s Office of Undergraduate Research, Scholarly and Creative Activity (URSCA), we run a very brief pair of Introduction to Python sessions for undergraduates who are on campus for summer research, as well as a one-session Introduction to Data Analysis.
You can access the notebooks at dbeller8/URSCA-Python. Links to launch them directly in Google Colab are given below.
Introduction to Python#
Part 1: tinyurl.com/ursca-python-1
basic data types, comparisons, logic
Part 2: tinyurl.com/ursca-python-2
control flow, functions, lists
Part 3: tinyurl.com/ursca-python-3
functions with multiple arguments, keyword arguments, and multiple return values
tuples, dictionaries, and sets
Mad Libs activity
Introduction to Data Analysis#
An introduction to scientific computation in soft matter and statistical physics#
These activities were developed and presented by the Beller Group at the CREST Science and Computing Research Summer Program / CCBM Virtual Sessions. The links below are to Jupyter Python notebooks that open and run in-browser through Google’s Colab platform. Each notebook contains a link to activity descriptions, designed for a high-school level and approximately 1-hour sessions.
Forest fire Colab notebook
Cahn-Hilliard Phase Separation (“Oil and Vinegar”, see screenshot below) Colab notebook
Disease Spread Model (SEIR) by Jimmy Gonzalez Nuñez Colab notebook
Tutorial: Free energy relaxation with finite differences#
This Python notebook was developed for a Short Course on “Computing Soft Matter Across Scales” run by the American Physical Society’s Division of Soft Matter at the 2023 APS March Meeting. At a graduate student level, it introduces the finite difference approach on a regular square grid and numerical free energy relaxation for conserved and non-conserved order parameters, progressing through:
Cahn-Hilliard model of immiscible fluids
Landau–Ginzburg theory of ferromagnetic ordering
Landau-de Gennes model of nematic liquid crystals
