Skip to content

Level-Set Curvature Neural Networks: A Hybrid Approach

License

Notifications You must be signed in to change notification settings

UCSB-CASL/HybridDLCurvature

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 

Repository files navigation

A Hybrid Inference System for Improved Curvature Estimation in the Level-Set Method Using Machine Learning

This is the accompanying repository for our hybrid inference system that approximates mean curvature in the level-set method. The neural networks here available were trained on the negative curvature spectrum, with samples from two-dimensional circular and sinusoidal interfaces. We considered 4 different mesh sizes: 2^{-7}, 2^{-8}, 2^{-9}, and 2^{-10}. We have grouped the corresponding models into 4 folders: 7, 8, 9, and 10. These numbers also represent the maximum levels of refinement of the quadtrees we used in our C++ parallel level-set library.

You'll find in each folder:

  • The trained model (in *.h5 format),
  • The pickled transformer object (PCA) for data preprocessing,
  • Some stats, and
  • Two plots comparing the numerical (scatterNumerics.png) and the neural (scatterReinit.png) approximations to mean curvature for (train + test + validation) samples obtained with level-set (10-iteration, PDE) reinitialization.

Feel free to forward your questions to this email.

About

Level-Set Curvature Neural Networks: A Hybrid Approach

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published