Dataset in support of the journal article 'Convolutional neural networks for mode on-demand high finesse optical resonator design'

Abstract

Dataset to support article "Convolutional Neural Networks for Mode On-Demand High Finesse Optical Resonator Design", accepted for publication in Scientific Reports This dataset contains the data of Figures 3 and 4, and the numerical model to recreate Figures 5-7of the associated publication The following data files are plain text files in CSV format (comma-separated values), readable by any text editor, spreadsheet editor, or numerical software (we use Microsoft Excel). All data descriptions and units are given as headers in the csv files. figure3.csv: data for Fig. 3, true and predicted amplitude and period (in um) of the validation dataset figure4.csv: data for Fig.4: mean error of the training dataset and of the validation dataset versus epoch number "one peak" and "two peaks" directories: Data in these directories contain the fully trained neural network structures and weights. Figures 5, 7 where generated from "one peak", Figure 6 from "two peaks". The model is stored in the standard Keras/Tensorflow format: The model architecture, and training configuration (including the optimizer, losses, and metrics) are stored in saved_model.pb. The weights are saved in the variables/ directory. For example, the "one peak" model can be loaded in Python by: from tensorflow import keras model = keras.models.load_model("one peak") The predicted pair of (Amplitude, Period) for a target field E (cooperativity C on a spatial grid in a 100x150 array, formatted to Keras/Tensorflow style) is then obtained as: predictions = model.predict(E)

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