Qc_study

Google Colab Edition of the Qc prediction study shared at https://github.com/simsekergun/Qc_Prediction

We predict the quality factor of the microrign resonators at high frequencies based on those resonators' quality factors at the lower frequencies using three different neural networks: fully-connected neural network (FCNN), recurrent neuural network (RNN), and attention mechanism.
We treat the prediction problem either as an interpolation (train-test data split is done randomly) or an extrapolation problem (test data includes features beyond the training data).
The original Qc dataset[https://github.com/simsekergun/Qc_datasets_functions] is generated by Gregory Moille.
The python code is developed by Masoud Soroush and edited by Ergun Simsek. Attention.py includes functions written by Edward Raff as well.

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
Extrapolation_Qc_with_Attention.ipynb		Extrapolation_Qc_with_Attention.ipynb
Extrapolation_Qc_with_FCNN.ipynb		Extrapolation_Qc_with_FCNN.ipynb
Extrapolation_Qc_with_RNN.ipynb		Extrapolation_Qc_with_RNN.ipynb
Interpolation_Qc_with_Attention.ipynb		Interpolation_Qc_with_Attention.ipynb
Interpolation_Qc_with_FCNN.ipynb		Interpolation_Qc_with_FCNN.ipynb
Interpolation_Qc_with_RNN.ipynb		Interpolation_Qc_with_RNN.ipynb
README.md		README.md

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