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Topics and dates for 2023

Homework 1 - Friday 8 September

  • Start of semester survey
  • Office hours poll

Lecture 1 - Friday 8 September

  • Introduction
  • What we hope to learn, what we expect to cover
  • Meeting each other
  • Why modeling?
    • discovery (choose better experiments [sensitivity and uncertainty analyses]; do the impossible [ask "what if?"])
    • design (predict and simulate)
  • Project scope

Homework 2 - Tuesday 12 September

  • Install Python (conda)
  • Register at GitHub

Lecture 2 - Tuesday 12 September

Lecture 3 - Friday 15 September

The projector wasn't working which was annoying. But there is a zoom recording.

  • Python
    • Numpy. Arrays
    • Loops
    • Functions
  • ODE solving
    • Differential equations, Simple Euler method to solve
    • Numerical Convergence. How and why and when.

Homework 3 - Tuesday 19 September

  • Some book reviews
  • Learn some Python

Lecture 4 - Tuesday 19 September

  • Project scope and planning
  • Attend Research showcase

Homework 4 - Friday 22 September

  • ODE solving - convergence.
  • Finish book reviews.

Lecture 5 - Friday 22 September

Homework

  • Adams-Bashforth

Lecture 6 - Tuesday 26 September

  • Project planning - making slide summaries

Homework

  • Runge Kutta 4 ? (not assigned)

Lecture 7 - Friday 29 September

  • ODE solving.
    • Numpy array bugs
    • Convergence of Adams Bashforth
    • Using SciPy's solve_ivp method
  • Kinetic Monte Carlo
    • how the rejection free algorithm works

Lecture 8 - Tuesday 3 October

  • Debugging! The 10 indispensible rules

Lecture 9 - Friday 6 October

  • Kinetic Monte Carlo - talking through the assignment

Lecture 10 - Tuesday 10 October

  • (at ECS conference)
  • Pair-coding

Lecture 11 - Friday 13 October

  • (at ECS conference)

Lecture 12 - Tuesday 17 October

  • Linear regression (scipy.stats.linregress)
  • Nonlinear regression (scipy.optimize.curve_fit)
  • Polynomial regression
  • Regression with uncertain x values (eg. scipy.odr)

Lecture 13 - Friday 20 October

  • Discussion of the regression homework
  • Start of PDEs and BVPs

Lecture 14 - Tuesday 24 October

  • PDEs and BVPs

Lecture 15 - Friday 27 October

  • Sensitivity analysis

Lecture 16 - Tuesday 31 October

  • Submarine problem

Lecture 17 - Friday 3 November

  • Submarine sensitivity analysis

Lecture 18 - Tuesday 7 November

  • Many people away at AIChE
  • Projects discussion.

Lecture 19 - Friday 10 November

  • LaTeX

Assignment: read the git parable

Lecture 20 - Tuesday 14 November

Lecture 21 - Friday 17 November

Lecture 22 - Tuesday 21 November

FALL BREAK - Friday 24 November

Lecture 23 - Tuesday 28 November

Lecture 24 - Friday 1 December

Lecture 25 - Tuesday 5 December

Checklists

The 2022 schedule is at https://github.com/CHME5137/Syllabus/blob/main/schedule2022.md

Homeworks

This is a list of possible homework assignments that I might pick from.

  • Bash
  • Book reviews
  • Rabbits and foxes diffusing
  • CodingBat Python practice
  • Runge-Kutta RK4 and convergence
  • Flesh out a project
  • Improve a project outline
  • Kinetic Monte Carlo
  • Regression
  • Git and github
  • Register for discovery
  • Sensitivity
  • [ ]

Topics

This is not a manifesto or contract, but a reminder list of things it would be cool to cover. i.e. it's too long and we won't cover them all.

  • Python
  • CodingBat
  • Convergence
  • ODEs
    • Simple Euler
    • RK4
    • SciPy
  • Kinetic Monte Carlo
    • Code optimization
  • PDEs
  • Debugging
  • Regression
  • Bayesian Parameter Estimation
  • Bash
  • Discovery cluster
  • LaTeX
  • Population Balance Modeling
  • Sensitivity Analysis
  • Cantera
  • Pandas (polyethylene?)
  • Machine Learning
  • VSCode
  • Programming with GPT and LLMs