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Random contractions (sub-unitary random matrices) appear naturally when considering quantized chaotic maps within a general theory of open linear stationary systems with discrete time. We analyze statistical properties of complex…

Chaotic Dynamics · Physics 2009-10-31 Yan V. Fyodorov , H. -J. Sommmers

Perturbation theory makes it possible to calculate the probability distribution function (PDF) of the large scale density field in the small variance limit. For top hat smoothing and scale-free Gaussian initial fluctuations, the result…

Astrophysics · Physics 2015-06-24 S. Colombi , F. Bernardeau , F. R. Bouchet , L. Hernquist

We investigated numerically the distribution of participation numbers in the 3d Anderson tight-binding model at the localization-delocalization threshold. These numbers in {\em one} disordered system experience strong level-to-level…

Disordered Systems and Neural Networks · Physics 2009-10-31 D. A. Parshin , H. R. Schober

The Thouless conjecture states that the average conductance of a disordered metallic sample in the diffusive regime can be related to the sensitivity of the sample's spectrum to a change in the boundary conditions. Here we present results…

Mesoscale and Nanoscale Physics · Physics 2009-10-28 D. Braun , E. Hofstetter , G. Montambaux , A. MacKinnon

The distribution function of local amplitudes of eigenstates of a two-dimensional disordered metal is calculated. Although the distribution of comparatively small amplitudes is governed by laws similar to those known from the random matrix…

Condensed Matter · Physics 2016-08-31 Vladimir I. Fal'ko , K. B. Efetov

We study the level spacing distribution for the spectrum of a point scatterer on a flat torus. In the 2-dimensional case, we show that in the weak coupling regime the eigenvalue spacing distribution coincides with that of the spectrum of…

Mathematical Physics · Physics 2014-03-24 Zeev Rudnick , Henrik Ueberschaer

We study signatures of quantum chaos in (1+1)D Quantum Field Theory (QFT) models. Our analysis is based on the method of Hamiltonian truncation, a numerical approach for the construction of low-energy spectra and eigenstates of QFTs that…

Statistical Mechanics · Physics 2021-04-02 Miha Srdinsek , Tomaz Prosen , Spyros Sotiriadis

Recent numerical simulations have shown that the distribution of conductance P(g) in 3D strongly localized regiem differs significally from the expected log normal distribution. To understand the origin of this difference analytically, we…

Disordered Systems and Neural Networks · Physics 2015-06-24 K. A. Muttalib. P. Markos , P. Woelfle

In the context of Monte Carlo simulations, the analysis of the probability distribution $P_L(m)$ of the order parameter $m$, as obtained in simulation boxes of finite linear extension $L$, allows for an easy estimation of the location of…

Soft Condensed Matter · Physics 2011-05-27 T. Fischer , R. L. C. Vink

Anderson localization of electron states on graphene lattice with diagonal and off-diagonal (OD) disorder in the absence of magnetic field is investigated by using the standard finite-size scaling analysis. In the presence of diagonal…

Disordered Systems and Neural Networks · Physics 2008-01-03 Shi-Jie Xiong , Ye Xiong

A new type of perturbation expansion in the mixing $V$ of localized orbitals with a conduction-electron band in the $U\to\infty$ Anderson model is presented. It is built on Feynman diagrams obeying standard rules. The local correlations of…

Condensed Matter · Physics 2009-10-22 Jan Brinckmann

The spectral form factor of random matrix theory plays a key role in the description of disordered and chaotic quantum systems. While its moments are known to be approximately Gaussian, corrections subleading in the matrix dimension, $D$,…

Quantum Physics · Physics 2026-01-06 Alex Altland , Francisco Divi , Tobias Micklitz , Silvia Pappalardi , Maedeh Rezaei

In this paper we give a physically transparent picture of singular-continuous spectrum in disordered systems which possess a non-ergodic extended phase. We present a simple model of identically and independently distributed level spacing in…

Disordered Systems and Neural Networks · Physics 2023-05-03 B. L. Altshuler , V. E. Kravtsov

A large i.i.d. random matrix with deterministic low-rank perturbation has been extensively studied, particularly in the aspects of the ESD (Empirical Spectral Distribution) and the outliers of eigenvalues. In this work, we investigate the…

Information Theory · Computer Science 2025-06-24 Kun Chen , Zhihua Zhang

We numerically investigate the distribution of Drude weights $D$ of many-body states in disordered one-dimensional interacting electron systems across the transition to a many-body localized phase. Drude weights are proportional to the…

Mesoscale and Nanoscale Physics · Physics 2016-11-23 Michele Filippone , Piet W. Brouwer , Jens Eisert , Felix von Oppen

We consider network models of quantum localisation in which a particle with a two-component wave function propagates through the nodes and along the edges of an arbitrary directed graph, subject to a random SU(2) rotation on each edge it…

Mathematical Physics · Physics 2009-11-10 John Cardy

The scaling theory of Anderson localization is based on a global conductance $g_L$ that remains a random variable of order O(1) at criticality. One realization of such a conductance is the Landauer transmission for many transverse channels.…

Disordered Systems and Neural Networks · Physics 2009-07-21 Cecile Monthus , Thomas Garel

We show how to use properties of the vectors which are iterated in the transfer-matrix approach to Anderson localization, in order to generate the statistical distribution of electronic wavefunction amplitudes at arbitary distances from the…

Disordered Systems and Neural Networks · Physics 2009-11-07 S. L. A. de Queiroz

We introduce a one dimensional disordered Ising model which at zero temperature is characterized by a non-trivial, non-self-averaging, overlap probability distribution when the impurity concentration vanishes in the thermodynamic limit. The…

Condensed Matter · Physics 2009-10-22 A. Crisanti , G. Paladin , M. Serva , A. Vulpiani

In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of real-time decision making systems, there is a need for safeguards that can detect out-of-training-distribution (OoD) inputs both efficiently and…

Machine Learning · Computer Science 2021-02-26 Apoorva Sharma , Navid Azizan , Marco Pavone