  The high demand for fabricating microresonators with desired optical
properties has led to various techniques to optimize geometries, mode
structures, nonlinearities and dispersion. Depending on applications, the
dispersion in such resonators counters their optical nonlinearities and
influences the intracavity optical dynamics. In this paper, we demonstrate the
use of a machine learning (ML) algorithm as a tool to determine the geometry of
microresonators from their dispersion profiles. The training dataset with ~460
samples is generated by finite element simulations and the model is
experimentally verified using integrated silicon nitride microresonators. Two
ML algorithms are compared along with suitable hyperparameter tuning, out of
which Random Forest (RF) yields the best results. The average error on the
simulated data is well below 15%.
