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Apply finite differences to Poisson’s equation to calculate electric potential from boundary conditions.
A visual recap of simple harmonic motion using sinusoidal, velocity, energy, and phase-space graphs.
Learn to import video into Tracker, analyze motion, calibrate length, create graphs, and automate point tracking for introductory physics.
Import experimental lab data from spreadsheets and databases into Jupyter with pandas, then plot it and change its labels.
Tests a projectile model against tracking data, using a basketball example to assess whether its predictions match real-world motion.
Use loops to vary a projectile’s properties systematically and graph the resulting differences in its trajectories.
Explore strange quantum-mechanical behavior by modeling spin and measurements with Python arrays.
Strategies for integrating computation into physics courses, including expanding the circle of instructors, creating space for computational content, and exploring CS pathways for effective teaching.
Learn Python basics in Jupyter Notebook, covering variables, complex numbers, and arrays. Designed for high school teachers to enhance quantum physics instruction.
A computational approach to teaching and assessing challenging quantum concepts in a junior-level modern physics course.
Explore the Lonely Runner Conjecture by simulating runners on a circle and tracking when each becomes sufficiently separated from the others.
Explore particle swarm behavior through interactive coding, simulating collective movement and emergent patterns in a dynamic visual environment.
Explore particle behavior through random walks using VPython, simulating and visualizing stochastic processes in physics.
Learn to visualize energy concepts through VPython coding, enhancing your understanding of physics principles and computational skills.
Learn to simulate drag force using VPython, enhancing your understanding of physics concepts through interactive coding and visualization techniques.
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