MultiSTOP: Solving Functional Equations with Reinforcement Learning

Alessandro Trenta; Andrea Cossu; Davide Bacciu; Pietro Ferrero

MultiSTOP: Solving Functional Equations with Reinforcement Learning

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arxiv 2404.14909 v1 pith:JVXUQCYF submitted 2024-04-23 cs.LG hep-th

Alessandro Trenta , Davide Bacciu , Andrea Cossu , Pietro Ferrero This is my paper · ORCID

classification cs.LGhep-th

keywords equationsfunctionallearningmultistopreinforcementsolvingaccuracyactual

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We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory.

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Probing Line Defect CFT with Mixed-Correlator Bootstrability
hep-th 2024-12 conditional novelty 6.0 of 10

Using mixed-correlator bootstrability with parity and localization input, this paper produces new finite-coupling bounds on 12 OPE coefficients and a new exact BPS structure constant for the Maldacena-Wilson line defect CFT.

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