The VPItoolkit ML Framework is a versatile addition to the simulation tools within the VPIphotonics Design Suite, enabling the implementation and design of deep neural networks (DNNs), recurrent neural networks (RNNs), and machine learning (ML) clustering techniques for a wide range of applications. These include equalization and nonlinearity mitigation in optical transmission systems, characterization, evaluation, optimization, and inverse design of photonic devices, as well as monitoring and optimization of network performance.

The VPItoolkit ML Framework offers a powerful set of capabilities that empower users to easily deploy custom-made ML algorithms. In addition to this flexibility, the framework provides a ready-to-use, open-source, Python-based ML solution with an intuitive, easy-to-use interface. This allows users to manipulate model parameters and convergence constraints with ease. The framework seamlessly integrates JupyterLab, providing users with a Cosimulation interface. This enables a streamlined workflow for prototyping and experimentation, allowing users to develop and test their ML models directly within the VPIphotonics simulation environment.

The framework is designed to enable users to build predictive models that can operate effectively in the presence of uncertainty. This is achieved by leveraging known training data sets, which are used to train neural networks models or other custom-made supervised ML models. In addition to the neural network capabilities, the framework also supports ML clustering. Unsupervised learning allow users to identify patterns and groupings within their data, without the need for predefined labels or target variables. This can be particularly useful for tasks such as enhanced equalization of optical systems, network optimization or anomaly detection, where the underlying relationships in the data may not be fully known a priori.

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