Study of nonlinear optical diffraction patterns using machine learning models based on resnet 152 architecture

HIGHLIGHTS

SUMMARY

In the last few decades, nonlinear optical materials attracted much attention due to their wide applications in optical modulators eye protectors,2 optical switchers,3 and mode-locking. In cases where the rings are not easily distinguishable due to various reasons, such as nonlinear phase change being inadequate, local thermal convection currents in aqua samples, or diffraction, an alternative method such as machine_learning can be extremely useful. Image classification as a branch of supervised machine_learning tries to distinguish between two (binary) or several types of images (categorical) by analyzing the images and discovering patterns . . .

 



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