MWF 11am sections in Boswell Hall 20

Eugeniy E. Mikhailov office hours on Wednesdays at 2pm.

If you want to meet with me outside of office hours, schedule an appointment

I am available at my office SmallHall 253, or in the superposition of my labs 032, 034, or 065.

MWF 1pm sections in Small Hall 111

Greg Bentsen office hours on Tuesdays at 10am in Small Hall 153

TA

Sam Bevins sjbevins@wm.edu office hours on Tuesdays and Thursdays 1-2pm in Small Hall 260

Syllabus

html pdf markdown

Tentative schedule and calendar

HW 01 due Monday 08/31.

Instructions available in pdf and tex format. Feel free to reuse parts of the tex file for your reports.

HW 02 due Monday 09/14.

Instructions available in pdf and tex format. Feel free to reuse parts of the tex file for your reports.

• have a look at APS journal style guide. The relevant section is “Figures”
  • they have good ideas and physics standards how to label and present units.
  • python actually not very good with scientific representation at the plots, we will see how to fix it later
• zip archive of templates for code preparation, submit your solutions to Gradescope in similarly organized zip archive
• data file to process hw02dataset.csv

You can convert the data file in relevant python variables using the following code

import numpy as np
# Load the dataset
data = np.loadtxt('hw02dataset.csv', delimiter=',', skiprows=1)
# Extract voltage and current columns
V = data[:, 0]
I = data[:, 1]

HW 03 due Monday 09/21.

Instructions available in pdf and tex format. Feel free to reuse parts of the tex file for your reports.

• datafiles:
  • hw02dataset.csv
  • hw_fit_cos_problem.dat
  • gaussian_beam.dat
  • data_to_make_fit_choice.dat
• zip archive of templates for code preparation, submit your solutions to Gradescope in similarly organized zip archive

HW 04 due Monday 10/19.

Instructions available in pdf and tex format. Feel free to reuse parts of the tex file for your reports.

HW 05 due Monday 10/26.

Instructions available in pdf and tex format. Feel free to reuse parts of the tex file for your reports.

Data file decay_data.csv

you will need the following code

• AI prompt which defined rules of the game Readme.md. Read it to have better grasp of the virus properties
• virus_model.py model in charge of virus properties
• virus_spread.py the virus spread simulation in the population
• visualizer_3d.py code which helps to visualize the situation

To perform simulation run

python virus_spread.py

above will create a 3d array with evolution snapshots in the file evolution_map.npy. To visualize the situation execute

python visualizer_3d.py --file evolution_map.npy

Do not change anything in the initialization of the map, the only parameters at your control are p_d and p_i which defined right before the call to virus_model.virusMove(...) called within a for loop in virus_spread.py

Lecture 01

Slides in html and markdown formats

Lab 01

Slides in html and markdown formats

Extra file:

• data file data_example.csv

Lecture 02

Slides in html and markdown formats

Lecture 03

Slides in html and markdown formats

Lab 02 (coincide with declared holiday)

Using python and an editor combo

Lab 03

Slides in html and markdown formats

Code archive: lab03_code.zip

• Basics of VS Code as and IDE

Lab 04

Slides in html and markdown formats

Code archive: lab04_code.zip

• Understanding error messaging and step by step debugging

Lecture 04

Slides in html and markdown formats

Code archive: lecture04_code.zip

• Nonlinear fitting
• Residuals

Lecture 05

Slides in html and markdown formats

Lab 05

Slides in html and markdown formats

Code archive: lab05_code.zip

• AI workflow for fitting

Lecture 06

Root finding algorithms

Slides in html and markdown formats

Lecture 07

Numerical integration

Slides in html and markdown formats

Lab 06

Based on hw 02 we prepared

• good report
  • here is screen cast showing what happen on screen of Prof. Mikhailov as he working on the report preparation
• sloppy for p1 and AI generated report

Lecture 08

Monte-Carlo numerical integration

Slides in html and markdown formats

Lecture 09

Root finding and numerical integral gotchas

Slides in html and markdown formats

Lab 07

Mock midterm

Midterm 1

Lecture 10

Iterated maps

Slides in html and markdown formats

Code archive: lecture10_code.zip

Lecture 11

From simulation to optimization

Slides in html and markdown formats

Code archive: lecture11_code.zip

Lecture 12

Numerical Optimization I: One Dimension

Slides in html and markdown formats

Code archive: lecture12_code.zip

Lecture 13

Numerical Optimization II: Multiple Dimensions

Slides in html and markdown formats

Code archive: lecture13_code.zip

Lecture 14

Numerical Optimization III: Gradient Descent

Slides in html and markdown formats

Code archive: lecture14_code.zip

Lecture 15

Numerical Optimization IV: Combinatorial optimization

• revisiting annealing

Slides in html and markdown formats

Code of backpack_binaray.py

Lecture 16

Numerical Optimization V: Combinatorial optimization

• revisiting backpack packing
• traveling salesman problem
• brute force vs simulated annealing optimization

Slides in html and markdown formats

Lecture 17

Numerical Optimization VI: Linear algebra and optimization

Slides in html and markdown formats