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Machine learning for optical sensing with grating nanostructures

Over the past decade, machine learning has found a large number of applications in physics. Machine learning algorithms can extract the most informative features of the data, reduce the dimensionality and increase the signal-to-noise ratio. This article discusses the use of machine learning algorithms to increase the accuracy of the optical sensors based on optical fiber Bragg grating sensor and the hydrogen sensor based on Wood anomaly in a diffraction grating. We show that application of machine learning algorithms to experimental data processing allows reaching high accuracy and reduce level of noise in optical sensors.