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In this paper we suggest an extension of the Rosenzweig-Porter (RP) model, the LN-RP model, in which the off-diagonal matrix elements have a wide, log-normal distribution. We argue that this model is more suitable to describe a generic many…

无序系统与神经网络 · 物理学 2020-12-14 I. M. Khaymovich , V. E. Kravtsov , B. L. Altshuler , L. B. Ioffe

Motivated by the problem of Many-Body Localization and the recent numerical results for the level and eigenfunction statistics on the random regular graphs, a generalization of the Rosenzweig-Porter random matrix model is suggested that…

无序系统与神经网络 · 物理学 2015-12-29 V. E. Kravtsov , I. M. Khaymovich , E. Cuevas , M. Amini

We consider the static and dynamic phases in a Rosenzweig-Porter (RP) random matrix ensemble with the tailed distribution of off-diagonal matrix elements of the form of the large-deviation ansatz. We present a general theory of survival…

无序系统与神经网络 · 物理学 2021-09-01 I. M. Khaymovich , V. E. Kravtsov

Rosenzweig-Porter (RP) model has garnered much attention in the last decade, as it is a simple analytically tractable model showing both ergodic--nonergodic extended and Anderson localization transitions. Thus, it is a good toy model to…

无序系统与神经网络 · 物理学 2023-12-12 Madhumita Sarkar , Roopayan Ghosh , Ivan M. Khaymovich

In this paper we consider an extension of the Rosenzweig-Porter (RP) model, the L\'evy-RP (L-RP) model, in which the off-diagonal matrix elements are broadly distributed, providing a more realistic benchmark to develop an effective…

无序系统与神经网络 · 物理学 2021-03-31 Giulio Biroli , Marco Tarzia

In recent years the Rosenzweig--Porter (RP) ensemble, obtained by adding a diagonal matrix with independent and identically distributed elements to a Gaussian random matrix, has been widely used as a minimal model for the emergence of…

We study the stability of non-ergodic but extended (NEE) phases in non-Hermitian systems. For this purpose, we generalize a so-called Rosenzweig-Porter random-matrix ensemble (RP), known to carry a NEE phase along with the Anderson…

无序系统与神经网络 · 物理学 2023-11-03 Giuseppe De Tomasi , Ivan M. Khaymovich

Dynamical and spatial correlations of eigenfunctions as well as energy level correlations in the Anderson model on random regular graphs (RRG) are studied. We consider the critical point of the Anderson transition and the delocalized phase.…

无序系统与神经网络 · 物理学 2019-01-10 K. S. Tikhonov , A. D. Mirlin

We study analytically and numerically the dynamics of the generalized Rosenzweig-Porter model, which is known to possess three distinct phases: ergodic, multifractal and localized phases. Our focus is on the survival probability $R(t)$, the…

无序系统与神经网络 · 物理学 2019-01-30 G. De Tomasi , M. Amini , S. Bera , I. M. Khaymovich , V. E. Kravtsov

A numerical study of Anderson transition on random regular graphs (RRG) with diagonal disorder is performed. The problem can be described as a tight-binding model on a lattice with N sites that is locally a tree with constant connectivity.…

无序系统与神经网络 · 物理学 2016-12-28 K. S. Tikhonov , A. D. Mirlin , M. A. Skvortsov

The delocalized non-ergodic phase existing in some random $N \times N$ matrix models is analyzed via the Wigner-Weisskopf approximation for the dynamics from an initial site $j_0$. The main output of this approach is the inverse…

无序系统与神经网络 · 物理学 2017-06-26 Cecile Monthus

For point patterns observed in natura, spatial heterogeneity is more the rule than the exception. In numerous applications, this can be mathematically handled by the flexible class of log Gaussian Cox processes (LGCPs); in brief, a LGCP is…

统计理论 · 数学 2019-10-10 Jiří Dvořák , Jesper Møller , Tomáš Mrkvička , Samuel Soubeyrand

The Anderson transition on random graphs draws interest through its resemblance to the many-body localization (MBL) transition with similarly debated properties. In this Letter, we construct a unitary Anderson model on Small-World graphs to…

无序系统与神经网络 · 物理学 2025-02-25 Weitao Chen , Ignacio García-Mata , John Martin , Jiangbin Gong , Bertrand Georgeot , Gabriel Lemarié

The Rosenzweig-Porter random matrix ensemble serves as a qualitative phenomenological model for the level statistics and fractality of eigenstates across the many-body localization transition in static systems. We propose a unitary…

无序系统与神经网络 · 物理学 2026-05-21 Wouter Buijsman , Yevgeny Bar Lev

The Rosenzweig-Porter model is a one-parameter family of random matrices with three different phases: ergodic, extended non-ergodic and localized. We characterize numerically each of these phases and the transitions between them. We focus…

无序系统与神经网络 · 物理学 2019-12-04 M. Pino , J. Tabanera , P. Serna

At Anderson critical points, the statistics of the two-point transmission $T_L$ for disordered samples of linear size $L$ is expected to be multifractal with the following properties [Janssen {\it et al} PRB 59, 15836 (1999)] : (i) the…

无序系统与神经网络 · 物理学 2009-05-28 Cecile Monthus , Thomas Garel

We propose a new viewpoint on the study of localization transitions in disordered quantum systems, showing how critical properties can be seen also as a geometric transition in the data space generated by the classically encoded…

无序系统与神经网络 · 物理学 2024-07-16 Carlo Vanoni , Vittorio Vitale

We study critical and universal behaviors of unitary invariant non-gaussian random matrix ensembles within the framework of the large-N renormalization group. For a simple double-well model we find an unstable fixed point and a stable…

高能物理 - 理论 · 物理学 2009-10-30 S. Higuchi , C. Itoi , S. M. Nishigaki , N. Sakai

We consider from the localization perspective the new critical phenomena discovered recently for perturbed random regular graphs (RRG) and constrained Erd\H{o}s-R\'enyi networks (CERN) \cite{crit2}. At some critical value of the chemical…

无序系统与神经网络 · 物理学 2018-04-10 V. Avetisov , A. Gorsky , S. Nechaev , O. Valba

Motivated by a series of recent works, an interest in multifractal phases has risen as they are believed to be present in the Many-Body Localized (MBL) phase and are of high demand in quantum annealing and machine learning. Inspired by the…

无序系统与神经网络 · 物理学 2024-01-17 Anton Kutlin , Ivan M. Khaymovich
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