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Topics

Lecture 1 (9/6)

  • Introduction
  • What we hope to learn, what we expect to cover

Lecture 2 (9/11)

  • Python (using ipython in browser)
  • Object types. (int/float/str/containers/boolean)
  • Duck typing.
  • Loops (for x in y:), control flow (if z:)

Lecture 3 (9/13)

  • Anaconda
  • IDEs
  • Jupyter
  • Differential equations, Simple Euler method to solve
  • Numerical Convergence

HW1

linux tutorial

Lecture 4 (9/18)

  • Kinetic Monte Carlo
  • Git intro - the principles (git parable)

HW3

  • Runge-Kutta RK4 and convergence
  • scipy.integrate.odeint

HW4

Register for github

Lecture 5 (9/20)

  • Git
  • Github
  • Practice resolving conflicts, adding name to github-assignment table

HW5

Submit a pull request (containing HW3 solution)

HW6

Book reviews

Lecture 6 (9/25)

  • discuss book reviews and resources for learning Python
  • how and why to generate .html and .py files
  • inplement post-save hook in jupyter config
  • interactive rebase to squash commits, and force push
  • discuss Runge-Kutta findings (should be 4th order)
  • discuss stiff ODE solvers (meaning of stiffness)
  • discuss projects

HW 7

Prepare a presentation/pitch describing a possible project idea.

Lecture 7 (9/27)

  • Project presentations

Lecture 8 (10/2)

  • More project presentations
  • Getting into groups for projects, and discussing

HW 8

Kinetic Monte Carlo for rabbits and foxes.

Lecture 9 (10/4)

  • git remotes, branches, pull requests, (for homework submission)
  • lists vs. numpy arrays
  • Making your code faster (when to optimize, how to benchmark, how to profile, etc.)
  • Kinetic Monte Carlo algorithm refresher (timesteps, events, etc.)
  • Convergence (how many KMC simulations required?)
  • Projects. Group assignments
  • Literature search: What has been done before (theory, models). What is the question to address?

no lecture on 10/9 due to holiday

HW 9

Literature Survey

Lecture 10 (10/11)

  • Linear regression (scipy.stats.linregress)
  • Nonlinear regression (scipy.optimize.curve_fit)
  • Regression with uncertain x values (eg. scipy.odr, though only discussed, not used)

HW 10

Regression https://github.com/CHME5137/regression

Lecture 11 (10/16)

Lecture 12 (10/18)

  • Regression homework
  • Debugging

Lecture 13 (10/23)

  • Mid-semester survey
  • Regression again
  • Discovery cluster (discussion/intro)
  • Sensitivity Analysis (principles/discussion)

Lecture 14 (10/25)

  • Sensitivity analysis

Lecture 15 (10/30)

Prof. West at AIChE conference.

  • Using discovery
  • Projects

Lecture 16 (11/1)

Prof. West at AIChE conference.

  • Using discovery
  • Projects

Lecture 17 (11/6 M)

Projects

Lecture 18 (11/8 W)

LaTeX

Lecture 19 (11/13 M)

Chemical Kinetics with Cantera

Lecture 20 (11/15 W)

  • Discuss the Reaction & Diffusion homework
  • Population Balance Modelling

Lecture 21 (11/20 M)

  • Population Balance Modelling
  • Projects

No class on 11/22 for Thanksgiving

Lecture 22 (11/27 M)

Lecture 23 (11/29 W)

Pandas for Polyethylene debugging

Lecture 24 (12/4 M)

Machine Learning

Lecture 25 (12/6 W)

Grade deadline 12/18

Remaining topics

  • Chemical Kinetics (cantera)
  • "Big" data (pandas)
  • Population balance models
  • Optimization
  • Machine Learning?