Journal
PHYSICAL REVIEW LETTERS
Volume 125, Issue 12, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.125.127401
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Funding
- U.S. Air Force Office of Scientific Research (AFOSR)
- Israel Science Foundation (ISF)
- European Research Council (ERC)
- European Union [802735]
- Binational Science Foundation USA-Israel (BSF)
- Adams Fellowship Program of the Israel Academy of Sciences and Humanities
- European Research Council (ERC) [802735] Funding Source: European Research Council (ERC)
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We demonstrate the identification of topological phase transitions from experimental data using diffusion maps: a nonlocal unsupervised machine learning method. We analyze experimental data from an optical system undergoing a topological phase transition and demonstrate the ability of this approach to identify topological phase transitions even when the data originates from a small part of the system, and does not even include edge states.
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