Name		Name	Last commit message	Last commit date
Data		Data
Figures/reply		Figures/reply
generative_NN		generative_NN
mesh		mesh
randomcxl		randomcxl
training_set		training_set
vortex_lines		vortex_lines
Figure_2D_spectrum_FFT.ipynb		Figure_2D_spectrum_FFT.ipynb
Figure_SVD.ipynb		Figure_SVD.ipynb
Figure_schematic_RW.ipynb		Figure_schematic_RW.ipynb
README.md		README.md
gridplot.ipynb		gridplot.ipynb
randomwave.py		randomwave.py
test_GPR_exp.ipynb		test_GPR_exp.ipynb
test_SQ_GPR.ipynb		test_SQ_GPR.ipynb
test_SQ_GPR_z.ipynb		test_SQ_GPR_z.ipynb
test_SQ_SVD.ipynb		test_SQ_SVD.ipynb
test_anisotropic_randomwave.ipynb		test_anisotropic_randomwave.ipynb
test_anisotropic_randomwave_MO.ipynb		test_anisotropic_randomwave_MO.ipynb
test_arbitary_k_distribution.ipynb		test_arbitary_k_distribution.ipynb
test_film_contrast.ipynb		test_film_contrast.ipynb
test_randomwave_visualize.ipynb		test_randomwave_visualize.ipynb
test_ring_average.ipynb		test_ring_average.ipynb

Repository files navigation

Randomwave_ML

About

Numerical methods can be employed to evaluate the scattering function of random wave structures, which can then be utilized as training sets for machine learning approaches to study microemulsions.

Readme

Activity

0 stars

1 watching

0 forks

Report repository

Navigation Menu

Repository files navigation

Randomwave_ML

About

Releases

Packages

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Randomwave_ML

About

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages