Exploring Deep Learning Models Aimed at Favorable Optimization and Enhancement of Fiber Optic Sensor’s Performance

We present a deep learning (DL)-assisted extrapolation approach for modeling a function that maximizes the performance of a fiber optic sensor (in terms of the figure of merit, i.e., FOM) under the variation of design parameters. We exploit a presumption that FOM should be treated as a relative entity. The recurrent neural network (RNN) framework is used to build the models that treat the data sequentially and relatively detect the hidden patterns. The objective of the adopted methodology is to forecast the best probable value of FOM for a particular combination of design parameters considered under finer resolutions. Our proposed model displays promising results on the test set and in the extrapolation corresponding to a subsequence length (
${l}$
) value of 35. Specifically, the sequential–sequential (Seq2Seq)-attention model’s performance on the test metrics is root-mean-square error (RMSE) = 2.12 ± 0.62, mean absolute error (MAE) = 0.54 ± 0.16, and symmetric mean absolute percentage error (SMAPE) = 0.92 ± 0.18. To verify the generality of our model for finer resolution data, we conducted tests for different values of wavelength. The results show that the model can capture the pattern and momentum of the FOM but marginally struggles to keep track of its magnitude for finer resolution data. However, the predictions for the training resolution are adequate. The processing time to predict FOM is nearly 1 s (68 ms/step), at least 100 times lesser than traditional simulation techniques. Our proposed network is relatively robust, converges quickly, has fewer parameters, and performs well on the test dataset. The analysis enables highly efficient and cost-effective optimization and enhancement of the fiber optic sensor’s performance.