schedule2022.md

Topics and dates for 2022

Lecture 1 Fri Sep 9

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

Homework 1 Fri Sep 9

• Start of semester survey.

Lecture 2 Tues Sep 13

• Python books
• Project planning
  • inputs, black box, outputs
• Python
  • types (int, float, str)
  • libraries
  • Anaconda
  • Jupyter lab
• Go to research Expo.

Homework 2. Fri Sep 16

• Book Reviews.

Lecture 3 Fri Sep 16

• Book reviews
• Python
  • Types (containers, iterables)
  • Loops (for x in y:), control flow (if z:)
  • Functions. Docstrings
  • Factorial

Homework 3

• Python learning

Lecture 4 Tues Sep 20

• ODE solving
  • Differential equations, Simple Euler method to solve
  • Numerical Convergence. How and why and when.

Homework 4

• ODE solver. Runge-Kutta 4 and solve_ivp

Lecture 5 Fri Sep 23

• Project planning
  • Making a 1-slide summary
  • Discussing the RK4 homework

Homework 5

• Project planning and literature search.

Homework 6

• Python learning

Lecture 6 Tues Sep 27

• Homework discussion
• Sharing what we learned in Python
• Data slicing in numpy arrays
• Kinetic Monte Carlo (rejection free algorithm)

Homework 7

• Read the Git Parable

Lecture 7 Fri Sep 30

• Git

Homework 8

• Kinetic Monte Carlo
  • this was a big one

Lecture 8 Tues Oct 4

• Kinetic Monte Carlo - how to do the homework.

Homework 9

• Git and Github

Lecture 9 Fri Oct 7

• Git homework
• Merging rebasing branches, pushing and pulling
• Reviewing pull requests
• Review of project summaries

Homework 10

• Python practice, and recap

Lecture 10 Tues Oct 11

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

Lecture 11 Fri Oct 14

• BVPs and PDEs

Homework 12

• Regression

Lecture 12 Tues Oct 18

• Regression homework discussion

Homework 13

• Read about debugging
• Fix up regression homeork

Lecture 13 Fri Oct 21

• Debugging

Lecture 14 Tues Oct 25

• Sensitivity

Homework 15

• PDEs

Lecture 15 Fri Oct 28

• Submarine kite turbine heat transfer

Lecture 16 Tues Nov 1

• LaTeX

Lecture 17 Fri Nov 4

• Submarine sensitivity (in groups)

Lecture 18 Tue Nov 8

Fri Nov 11 - Veterans' Day

No class - Veteran's Day

Lecture 19 Tue Nov 15

Prof West at AIChE Conference Work on your projects.

Lecture 20 Fri Nov 18

Prof West still at AIChE Conference Work on your projects.

Lecture 21 Tue Nov 22

Projects, Bash, Linux, Discovery, general discussion

Fri 25 Nov - Thanksgiving Break

Lecture 22 Tue Nov 29

Bayesian Parameter Estimation

Lecture 23 Fri Dec 2

Population Balances

Lecture 24 Tue Dec 6

Final Project Presentations

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