  One of the key features of non-Hermitian systems is the occurrence of
exceptional points (EPs), spectral degeneracies where the eigenvalues and
eigenvectors merge. In this work, we propose applying neural networks to
characterize EPs by introducing a new feature -- summed phase rigidity (SPR).
We consider different models with varying degrees of complexity to illustrate
our approach, and show how to predict EPs for two-site and four-site gain and
loss models. Further, we demonstrate an accurate EP prediction in the
paradigmatic Hatano-Nelson model for a variable number of sites. Remarkably, we
show how SPR enables a prediction of EPs of orders completely unseen by the
training data. Our method can be useful to characterize EPs in an automated
manner using machine learning approaches.
