ZooBeasts/cWGAN-GP_Inverse_Design_Disordered_Waveguide_Nanophotonics
This code is for of inverse design and forward prediction of disordered waveguide. Full codes updated
This project helps photonics researchers and engineers rapidly design and predict the behavior of disordered optical waveguides. You provide target optical transmission spectra, and it generates novel waveguide geometries. Alternatively, input existing geometries to predict their transmission spectra. This tool is for scientists and engineers working on nanophotonics and optical device design.
No commits in the last 6 months.
Use this if you need to quickly explore a wide range of disordered waveguide designs or predict their optical properties without extensive, time-consuming simulations.
Not ideal if you require highly precise, fine-tuned waveguide designs for very specific, narrow-band applications, as this model prioritizes diversity and generalization.
Stars
12
Forks
4
Language
Python
License
MIT
Category
Last pushed
Dec 20, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ZooBeasts/cWGAN-GP_Inverse_Design_Disordered_Waveguide_Nanophotonics"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
kaanaksit/odak
Scientific computing library for optics, computer graphics and visual perception.
NVIDIA/torch-harmonics
Differentiable signal processing on the sphere for PyTorch
PreFab-Photonics/PreFab
Artificial nanofabrication of integrated photonic circuits using deep learning
MatthewFilipovich/torchoptics
Differentiable wave optics simulation library built on PyTorch
artificial-scientist-lab/XLuminA
XLuminA, a highly-efficient, auto-differentiating discovery framework for super-resolution microscopy.