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From the 1 of 5 papers with an AI index.

most citedA Bayesian approach to out-of-sample network reconstruction

1 citations

5 papers

math-ph2026

Dirac Fields in Hydrodynamic Form and their Thermodynamic Formulation

Luca Fabbri, Stefano Vignolo, Giuseppe De Maria +1

The paper rewrites Dirac spinor fields in a polar (hydrodynamic) form using a 1+1+2 covariant split, decomposes the resulting equations, and illustrates the approach with examples…

math.NA2026

General Order Virtual Element Approximation for the Smagorinsky turbulence model

Stefano Berrone, Karol L. Cascavita, Enrique Delgado Ávila +3

In this paper, we investigate a Smagorinsky model in a virtual element framework to simulate convection-dominated Navier-Stokes equations. We conduct a two-dimensional numerical in…

physics.soc-ph2026

Community detection in subject-subject networks from psychometrics data

Arianna Armanetti, Luca Cecchetti, Eiko Fried +2

Identifying subgroups of respondents in psychometric data is traditionally addressed with Latent Class Analysis, which requires the number of classes to be specified a priori and c…

physics.soc-ph20261 cited

A Bayesian approach to out-of-sample network reconstruction

Mattia Marzi, Tiziano Squartini

Networks underpin systems that range from finance to biology, yet their structure is often only partially observed. Current reconstruction methods typically fit the parameters of a…

hep-th2026

Thermodynamics of magnetized BPS baryonic layers and the effects of the Isospin chemical potential

Sergio Luigi Cacciatori, Fabrizio Canfora, Evangelo Delgado +2

Through the Hamilton-Jacobi equation of classical mechanics, BPS magnetized Baryonic layers (possessing both baryonic charge and magnetic flux) have been constructed in the gauged…