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Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

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Figure

Fig. 3: The flow chart of ANN implementation.
Fig. 6: Variation of (a) neff and (b) Pconf with waveguide width for differentactivation functions at waveguide height = 225 nm using training dataset-3.
Fig. 7: Mean squared error (msegressor and PyTorch (b) training, validation, and test dataset-3 for PyTorch,) using (a) training dataset-3 for MLPRe-having 2 hidden layers with 50 nodes in each layer.
Fig. 10: Slot waveguide design predicting Pconf at waveguide height = 225nm with (a) PyTorch using dataset-1, (b) MLPRegressor using dataset-1, (c)
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