Learning to Trace Seiberg Dualities
Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems. In practice, though, it can often be computationally challenging to establish when two systems are dual, even when all of the "rules of the game" are well-known. Said differently, when confronted with two systems, how can one efficiently establish that they are in fact dual? In this paper we use machine learning methods to address this question for Seiberg dualities of supersymmetric quiver gauge theories. Mathematically, this involves establishing mutations of quivers, which is in turn a variation on the theme of "learning to unknot". On the one hand, this leads u...
| Authors | Jonathan J. Heckman, Shani Meynet, Alessandro Mininno et al. |
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| Primary category | hep-th |
| Published | 30 July 2026 |
| Last updated at source | 29 August 2026 |
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