Machine learning prediction of the convergence criterion for a topological invariant of finite non-Hermitian chains
This paper demonstrates that machine learning, specifically random-forest regression, can accurately predict the optimal crop-length parameter for calculating topological invariants in finite non-Hermitian chains by linking it to physical decay lengths and characteristic polynomial structures, thereby providing a robust, generalizable, and disorder-resilient method to capture topology near phase transitions.