Volume 144, Number 3, November 2023
|Number of page(s)
|Condensed matter and materials physics
|06 December 2023
Characterizing exceptional points using neural networks
Solid State and Structural Chemistry Unit, Indian Institute of Science - Bangalore 560012, India
Received: 27 May 2023
Accepted: 14 November 2023
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.
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