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OptiDistillNet: Learning nonlinear pulse propagation using the student-teacher model.

Naveenta Gautam, Vinay Kaushik, Amol Choudhary

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    Summary

    We developed OptiDistillNet, a compressed deep learning model for nonlinear fiber optics. This method significantly reduces model size while maintaining high accuracy for pulse evolution prediction.

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    Area of Science:

    • Nonlinear Optics
    • Computational Physics
    • Machine Learning

    Background:

    • Deep convolutional neural networks (CNNs) are effective for modeling complex physical phenomena.
    • Model compression is crucial for deploying deep learning models in real-world applications, particularly in resource-constrained environments.
    • Knowledge distillation (KD) is a promising technique for compressing large neural networks into smaller, more efficient ones.

    Purpose of the Study:

    • To develop a compressed CNN model for learning pulse evolution in nonlinear fibers by solving the nonlinear Schrodinger equation (NLSE).
    • To apply a knowledge distillation (KD) framework to compress a CNN, creating a student network named OptiDistillNet.
    • To evaluate the performance of the compressed model in terms of accuracy, convergence speed, and parameter efficiency.

    Main Methods:

    • Solving the nonlinear Schrodinger equation (NLSE) to generate training data for pulse evolution.
    • Implementing a knowledge distillation (KD) framework to train a smaller student network (OptiDistillNet) from a larger teacher network.
    • Utilizing a deep convolutional neural network (CNN) architecture for learning the pulse evolution dynamics.

    Main Results:

    • The OptiDistillNet achieved a mean square error (MSE) close to the teacher model's MSE of 1.04*10-5, even with a model size reduction of up to 91.2%.
    • The compressed student network demonstrated better generalization, faster convergence, and required fewer trainable parameters compared to the original model.
    • This represents the first application of KD techniques to nonlinear optics for model compression.

    Conclusions:

    • The proposed KD-based compression framework (OptiDistillNet) is highly effective for nonlinear optics applications.
    • The simplified architecture, fast optimization, and improved accuracy make OptiDistillNet suitable for practical applications, such as optical coherent communication systems.
    • This work paves the way for efficient deep learning deployment in complex nonlinear optical systems.