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
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
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VPItoolkit ML Framework supports the storage of large, complex,
heterogeneous data in an open-source file format (Hierarchical
Data Format version 5, HDF5).