2018
DOI: 10.3390/e20010025
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Exact Renormalization Groups As a Form of Entropic Dynamics

Abstract: Abstract:The Renormalization Group (RG) is a set of methods that have been instrumental in tackling problems involving an infinite number of degrees of freedom, such as, for example, in quantum field theory and critical phenomena. What all these methods have in common-which is what explains their success-is that they allow a systematic search for those degrees of freedom that happen to be relevant to the phenomena in question. In the standard approaches the RG transformations are implemented by either coarse g… Show more

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Cited by 15 publications
(16 citation statements)
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“…The distribution ρ for eigenvalues of the inverse matrix can be easily deduced from (40). Figure 6 provides a picture of the numerical flow for a quartic truncation.…”
Section: Analytical Noisy Data Mp Distributionmentioning
confidence: 99%
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“…The distribution ρ for eigenvalues of the inverse matrix can be easily deduced from (40). Figure 6 provides a picture of the numerical flow for a quartic truncation.…”
Section: Analytical Noisy Data Mp Distributionmentioning
confidence: 99%
“…Note that, in regimes where momenta take large values and DE breaks down, other approximation schemes exist, enabling to keep the full dependence of the effective vertices. The most popular being the so-called Blaizot–Mendez–Wschebor (BMW) method [ 21 , 22 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], which, combined with exact relations such as Ward identities, gives the possibility to provide exact (i.e., scheme independent) results [ 61 ]. Another current source of disagreement concerns the choice of the regulator.…”
Section: Concluding Remarks and Open Issuesmentioning
confidence: 99%
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“…ED has been successful in reconstructing dynamical models in physics such as quantum mechanics [28,29], quantum field theory [30], and the renormalization group [31]. Beyond physics, it has been recently applied to the rhw fields of finance [32,33] and neural networks [34].…”
Section: Introductionmentioning
confidence: 99%