nanophotonic-structures

Official repository of "Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations"

https://github.com/jungtaekkim/nanophotonic-structures

Science Score: 54.0%

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Repository

Official repository of "Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations"

Basic Info

Host: GitHub
Owner: jungtaekkim
License: mit
Language: Python
Default Branch: main
Homepage: https://jungtaekkim.github.io/nanophotonic-structures/
Size: 17.2 MB

Statistics

Stars: 5
Watchers: 2
Forks: 1
Open Issues: 0
Releases: 0

Created over 2 years ago · Last pushed almost 2 years ago

Metadata Files

Readme Contributing License Code of conduct Citation

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations

It is an official repository of the paper entitled "Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations", which is presented at NeurIPS 2023 Datasets and Benchmarks track.

Directories

nanophotonic_structures: a directory for nanophotonic structures and utils
scripts: a directory for runnable scripts
src: a directory for dataset generation, model training, model testing, and structure optimization

Scripts

generate_datset.sh: a script for dataset generation
optimizemodelscontinuous.sh: a script for nanophotonic structure optimization over continuous spaces
optimizemodelsdiscrete.sh: a script for nanophotonic structure optimization over discrete spaces
train_models.sh: a script for surrogate model training
test_models.sh: a script for surrogate model testing

Installation

Before installing our package, you should install meep first. A guide to the installation of meep is provided in this link.

Our package can be installed from source or from source in an editable mode.

console pip install . or console pip install -r requirements.txt python setup.py develop

Access to Datasets

If you want to create datasets by yourself, you can use scripts in the scripts directory.

Our datasets can be accessed via the Hugging Face repository.

Citation

@inproceedings{KimJ2023neuripsdb, author={Kim, Jungtaek and Li, Mingxuan and Hinder, Oliver and Leu, Paul W.}, title={Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, volume={36}, pages={4685--4715}, year={2023}, note={Datasets and Benchmarks Track} }

Instructions to Contribute to the Project

We are open to any users who want to contribute to the project. You can refer to instructions to know how you can contribute to the project.

License

It is licensed under the MIT license.

Code of Conduct

We follow the code of conduct to create a diverse, inclusive, and postive community.

Owner

Name: Jungtaek Kim
Login: jungtaekkim
Kind: user
Location: United States

Website: https://jungtaek.github.io
Twitter: jungtaek_kim
Repositories: 4
Profile: https://github.com/jungtaekkim

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite it.
title: Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
url: https://github.com/jungtaekkim/nanophotonic-structures
authors:
- family-names: Kim
  given-names: Jungtaek
- family-names: Li
  given-names: Mingxuan
- family-names: Hinder
  given-names: Oliver
- family-names: Leu
  given-names: Paul W.
preferred-citation:
  type: conference-paper
  title: Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
  authors:
    - family-names: Kim
      given-names: Jungtaek
    - family-names: Li
      given-names: Mingxuan
    - family-names: Hinder
      given-names: Oliver
    - family-names: Leu
      given-names: Paul W.
  collection-title: Advances in Neural Information Processing Systems (NeurIPS)
  collection-type: proceedings
  volume: 36
  start: 4685
  end: 4715
  year: 2023
  publisher:
    name: Curran Associates, Inc.
  url: https://arxiv.org/abs/2310.19053
  address: New Orleans, Louisiana, USA

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Dependencies

requirements.txt pypi

Py-BOBYQA *
bayeso *
h5py *
huggingface_hub *
matplotlib *
numpy *
pvlib *
scikit-learn *
torch *

setup.py pypi

ecosyste.ms

Data

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Open Source Science