FiodarM/InvDesignNet
Training neural networks for inverse design of nanophotonic gratings.
This project helps optical engineers and material scientists design nanophotonic gratings more efficiently. You provide desired optical properties, and it outputs the physical grating structures that achieve those properties. This is for researchers and engineers working on advanced optical materials and devices.
No commits in the last 6 months.
Use this if you need to quickly find the physical design parameters for nanophotonic gratings based on specific optical performance requirements, without extensive trial-and-error simulations.
Not ideal if you are looking for a general-purpose simulation tool for photonic devices or if your primary interest is in forward modeling (predicting optical properties from known structures).
Stars
20
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Dec 15, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/FiodarM/InvDesignNet"
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.