VPItoolkit ML Framework is a versatile add-on to any of the simulation tools of VPIphotonics Design Suite, enabling the implementation and design of deep neural networks (DNN) for various applications, such as equalization and nonlinearity mitigation for optical systems, device characterization, evaluation and inverse design of photonic devices.

This powerful framework allows the user to easily deploy custom-made machine learning (ML) algorithms. Additionally, it provides a ready-to-use open-source Python-based DNN with an intuitive easy-to-use interface to manipulate model parameters and convergence constraints.

The aim of VPItoolkit ML Framework is to allow the user to build a model that makes predictions based on evidence in the presence of uncertainty, by collecting known training data sets, which are used to train the DNN model or other supervised custom-made models.

Seamless manipulation of digital, electrical and optical signals is feasible, using the framework's flexible Data Extractors and Model Loaders. With a user-friendly access to the DNN hyperparameters, fast optimization can be performed for model performance enhancement.

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