governing-equations

This project is a in-class study case to understand SINDy algorithm.

This sparse identificiation method is developed by Dr. Steven L. Brunton et.al

citation1: Discovering governing equations from data by sparse identification of nonlinear dynamical systems, PNAS
citation2: Inferring Biological Networks by Sparse Identification fo Nonlinear Dynamics, (Brunton Group, Niall Mangan), IEEE Transactions on Molecular, Biological and Multi-Scale Communications

Here, we transimitted this method from MATLAB to python, and also applied this method to several biological systems.

Denoise algorithms and new information criteria of model selection were proposed in this code.

There are several documents is in good shape (final version):

Utlis.py contains all functions/ algorithms that is utlized in system identification
Four .ipynb, each one shows one example of the governing equation.

Others are the reduancy files(drafts)

Name		Name	Last commit message	Last commit date
.idea		.idea
.ipynb_checkpoints		.ipynb_checkpoints
.vscode		.vscode
__pycache__		__pycache__
figures		figures
final_v1		final_v1
.DS_Store		.DS_Store
ABR.ipynb		ABR.ipynb
GBCB_5874_report.pdf		GBCB_5874_report.pdf
Hill_function.py		Hill_function.py
Lorenz (changed params)_SEIR_RSM.ipynb		Lorenz (changed params)_SEIR_RSM.ipynb
Lorenz (changed params)_SEIR_RSM.py		Lorenz (changed params)_SEIR_RSM.py
Lorenz.py		Lorenz.py
MM.py		MM.py
MM_2.py		MM_2.py
MM_3.py		MM_3.py
MM_noise.ipynb		MM_noise.ipynb
MM_noise.py		MM_noise.py
README.md		README.md
RSM.py		RSM.py
RSM_test.py		RSM_test.py
SEIR_Xi		SEIR_Xi
coeff_convert.py		coeff_convert.py
graphs generator.py		graphs generator.py
new_ABR.ipynb		new_ABR.ipynb
new_abr.py		new_abr.py
optimal_SVHT_coef.ipynb		optimal_SVHT_coef.ipynb
output		output
output.csv		output.csv
output2.csv		output2.csv
sparesedynamic.py		sparesedynamic.py
utils.py		utils.py

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