pra23suppl
Supplementary materials for just submitted article: Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofins And Annular Groove Phase Masks
Science Score: 44.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (3.8%) to scientific vocabulary
Repository
Supplementary materials for just submitted article: Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofins And Annular Groove Phase Masks
Basic Info
- Host: GitHub
- Owner: Kaeryv
- License: unlicense
- Language: Python
- Default Branch: main
- Homepage: https://kaeryv.be
- Size: 169 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ACSPhot23Suppl
Supplementary materials for just submitted article: Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofins And Annular Groove Phase Masks
Launching on your system
If you cannot access a cluster with slurm workload manager, I recommand you just switch all sbatch instances to bash to run scripts locally. You will certainly want to adapt a bit those scripts but everything should work out of the box.
The project.yml will give these designs for 10 epochs and the seed.
bash
python scripts/run_surrogate.py -project project.yml -epochs 10
Owner
- Login: Kaeryv
- Kind: user
- Repositories: 1
- Profile: https://github.com/Kaeryv
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Roy
given-names: Nicolas
orcid: https://orcid.org/0000-0001-5417-2834
title: "PRA23Suppl: Reproducibility SI for 'Photonic Structures Optimization Using Highly Data-Efficient Deep Learning:
Application To Nanofin And Annular Groove Phase Masks'"
version: 0.1.0
date-released: 2023-10-03
url: https://github.com/Kaeryv/PRA23Suppl/