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computational physics class taught at UNLV (Phys300)

Jupyter Notebook 100.00%

python computational-physics machine-learning

computationalphysics300's Introduction

Physics 300, University of Nevada Las Vegas

Introduction to Physics and Scientific Computing

Location: BPB-248

Schedule: Mon/Weds 2:30 - 3:45 pm

Instructor	Prof. Qiang Zhu
Email	[email protected]
Website	http://www.physics.unlv.edu/~qzhu/
Office	BPB 232
Office hours	Mon/Weds 9 - 10 am

Course Outline

Weeks	Subjects
1	Python basics I (installation, variables, list, loops)
2	Python basics II (function, advanced libraries)
3	Integrals/derivatives
4	Fitting/interpolation
5	Fourier transform
6	Random numbers
7	Monte carlo
8	Optmization I
9	Optmization II
10	Optmization III
11	Machine Learning I (Algorithms)
12	Machine Learning II (Applications)
13	Machine Learning III (Neural Networks)

Prerequisites: PHYS 152, PHYS 152L or PHYS 180

Credit Hours: 3

Textbook: Computational Physics, M. Newman (not required)

Grade Distribution:

Items	Percentage
Attendance	10%
Homeworks	25%
Midterm Project	25%
Final Project	40%

Course Description

This course is open to all students who are interested in scientific programming and data analysis. It will teach students to write programs to solve simple physics programs on the computer and to manage their codes via github. There will be weekly assignments and two projects during the semester, plus an oral exam in the end of semester. Please bring your laptop to class. All the practices will be based on Python 3. Barring documentable emergencies or observance of a certifable regious holiday, all exams must be taken at the time and place specified.

Appendix

In addtion to the code page, we also have a wiki page which has extended discussions on some focused topics. Most of them were created by the students.

Notebook viewer

Sometimes the GitHub does not render the jupyter notebook properly. We recommend the use of nbviewer to view the notebook.

Instruction to submit your homework.

register an account in GitHub.
log into your github profile, and search for the github repo of qzhu2017/ComputationalPhysics300/, click the fork icon.
after your log into your github account, you will have the forked github repo.
complete homework by your own in the format of jupiter notebook. Upload the notebook to the homework directory by time.

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computationalphysics300's Issues

add complex step derivative

to Lec 06. Compare it with finite difference method.

add LJ force in cluster optimization

knowing the gradient would help

Random number generation

The choice of m seems to generate very interesting phenomena.

%matplotlib inline
import matplotlib.pyplot as plt 
# We can only change [a , c , m]
N = 1000
a = 3125 #57
c = 19*17 #287
m = 10.99
x = 1
# This shows that a has to be in range 40<a<60 and c has to be in range of 200<c<300 for it to be somewhat random
results = []

for i in range(N):      
    x = (a*x+c)%m -5
    results.append(round(x))
plt.plot(results,"o")
plt.show()
plt.hist(results, bins=10)

add decorator

add a section of line search in optimization

add a project page

add labelencoder from sklearn

Add Newton and secant method in the derivative lecturue

Can be used to solve the root of single variable function f(x)=0.
f(x) = exp(x)*lnx - x**2

find the bond length of NaCl
V(r) = -e^2/r + ae^{-r/p}
a = 1.0910^3 eV
p = 0.330
e2 = 14.4
Tao Pang, pp 37

to add MD lecture

The origin of parameter set for random number generation

I got the set of numbers {a, c, m} in Lec 15 from Pang's book (Computational Physics), but haven't figured out its real origin yet.

add a lecture to fully explain all algos

https://blog.csdn.net/golden1314521/article/details/46225289

Expand the neural network code

Currently, we have a single neuron code, but it would be good to expand it 2 neurons, 2 layers. This way, we will have a better understanding on the back-propagation mechanism used in the updates of the weights.

add animation

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