There will be between 10-14 units in this course. This will be adjusted as the course goes on.

Unit 1: N Ways To Generate PI

Learning objectives: Familiarity with Jupyter notebooks; Being able to program loops and list comprehensions; Plotting data; Understanding and using random numbers; Developing an intuition for stochastic errors; Limits and Series;

Unit 2: The Dynamics of Throwing Balls into the Air

Learning objectives: Remembering dynamics; Differential Equations as a route to understand scientific phenomena; Air Resistance; Programming Functions; Understanding abstraction of computational components; Validating code; Euler Integration and errors associated with them; Plotting on logarithmic scales;

Unit 3: Improved Dynamics and Springs

Learning objectives: Programming and using classes; Midpoint formulas for differential equations and their associated errors; Learning how to modify small parts of a code to change the physics/more abstraction; Damped harmonic oscillators; Pendulums; Forcing functions;

Unit 4: Orbital Dynamics

Learning objectives: Developing two-dimensional dynamics code; gravitational forces; central potentials; Kepler's laws; analyzing data; orbital dynamics;

Unit 5: Mercury Perihelion

Learning objectives: General relativity; machine precision;

Unit 6: Exoplanets

Learning objectives: Fourier transforms; analyzing real data; basic data I/O techniques & signal processing;

Unit 7: Predator-Prey

Learning objectives: biophysics; differential equations for other systems; ecosystems; continuous time markov chains; stochastic methods

Unit 8: Random Walks

Learning objectives: Random walks; stochasticity;

Unit 9: Fluid Dynamics

Learning objectives: Fluid dynamics; animation; turbulence; convention

Unit 10: Markov Chains

Learning objectives: Markov chains; Metropolis Algorithm; Boltzmann distribution;

Unit 12: Particle Physics

Learning objectives: Particle Physics; simulation of a particle physics experiment

Unit 10: Quantum Computing

Learning objectives: quantum computing

Final Exam

  • The exam notebook



Extra Credit 1:

Learning objectives: markov chains; integration; symbolic computation

Extra Credit 2:

Learning objectives: General Relativity;

Extra Credit 3:

Learning objectives: General Relativity; Embedding Diagrams
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