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The propagation of a spherical wave through a two-dimensional random Lorentz gas composed of small fixed scatterers is studied. Inspired by the Mott problem (how an initially isotropic quantum wave can give rise to a single particle-like…

Quantum Physics · Physics 2026-03-16 Baptiste Lorent , Jean-Marc Sparenberg , David Gaspard

A Riemannian stochastic representation of model uncertainties in molecular dynamics is proposed. The approach relies on a reduced-order model, the projection basis of which is randomized on a subset of the Stiefel manifold characterized by…

Computational Physics · Physics 2022-10-27 Hao Zhang , Johann Guilleminot

We obtain conditions for the differentiability of weak solutions for a second-order uniformly elliptic equation in divergence form with a homogeneous co-normal boundary condition. The modulus of continuity for the coefficients is assumed to…

Analysis of PDEs · Mathematics 2016-02-18 Robert McOwen , Vladimir Maz'ya

We survey recent mathematical results about the spectrum of random band matrices. We start by exposing the Erd{\H o}s-Schlein-Yau dynamic approach, its application to Wigner matrices, and extension to other mean-field models. We then…

Probability · Mathematics 2018-08-31 Paul Bourgade

We consider a general class of $n\times n$ random band matrices with bandwidth $W$. When $W^2\ll n$, we prove that with high probability the eigenvectors of such matrices are localized and decay exponentially at the sharp scale $W^2$.…

Probability · Mathematics 2025-08-29 Reuben Drogin

We prove that a strong form of dynamical localization follows from a variable energy multi-scale analysis. This abstract result is applied to a number of models for wave propagation in disordered media.

Mathematical Physics · Physics 2014-12-30 D. Damanik , P. Stollmann

We review recent results on many-body localization for two explicitly analyzable models of many-body quantum systems, the XY spin chain in transversal magnetic field as well as interacting systems of harmonic quantum oscillators. In both…

Mathematical Physics · Physics 2018-01-03 Robert Sims , Gunter Stolz

The Lipschitz constant is an important quantity that arises in analysing the convergence of gradient-based optimization methods. It is generally unclear how to estimate the Lipschitz constant of a complex model. Thus, this paper studies an…

Machine Learning · Statistics 2023-02-10 Calypso Herrera , Florian Krach , Josef Teichmann

Multivariate regular variation plays a role assessing tail risk in diverse applications such as finance, telecommunications, insurance and environmental science. The classical theory, being based on an asymptotic model, sometimes leads to…

Probability · Mathematics 2011-08-31 Bikramjit Das , Abhimanyu Mitra , Sidney Resnick

This work introduces the minimax Laplace transform method, a modification of the cumulant-based matrix Laplace transform method developed in "User-friendly tail bounds for sums of random matrices" (arXiv:1004.4389v6) that yields both upper…

Probability · Mathematics 2011-07-22 Alex Gittens , Joel A. Tropp

Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation. We bound the distributionally robust risk for a model class…

Machine Learning · Statistics 2018-09-06 Zac Cranko , Simon Kornblith , Zhan Shi , Richard Nock

We characterise and study dynamical localisation of a finite interacting quantum many-body system. We present explicit bounds on the disorder strength required for the onset of localisation of the dynamics of arbitrary ensemble of sites of…

Mathematical Physics · Physics 2014-02-07 P. -L. Giscard , Z. Choo , M. T. Mitchison , J. J. Mendoza-Arenas , D. Jaksch

We study discrete random Schr\"odinger operators via the supersymmetric formalism. We develop a cluster expansion that converges at both strong and weak disorder. We prove the exponential decay of the disorder-averaged Green's function and…

Mathematical Physics · Physics 2020-10-15 Luca Fresta

The present paper is devoted to the study of spectral properties of random Schroedinger operators. Using a finite section method for Toeplitz matrices, we prove a Wegner estimate for some alloy type models where the single site potential is…

Mathematical Physics · Physics 2007-05-23 Vadim Kostrykin , Ivan Veselic

We present a full analytical solution for the localisation length in the one-dimensional Anderson model with weak diagonal disorder in the vicinity of the band centre. The results are obtained with the Hamiltonian map approach that turns…

Disordered Systems and Neural Networks · Physics 2012-06-01 L. Tessieri , I. F. Herrera-González , F. M. Izrailev

We prove Anderson localization at the internal band-edges for periodic magnetic Schr{\"o}dinger operators perturbed by random vector potentials of Anderson-type. This is achieved by combining new results on the Lifshitz tails behavior of…

Mathematical Physics · Physics 2007-08-15 F. Ghribi , P. D. Hislop , F. Klopp

Results of large-scale numerical simulations are reported on the Anderson localization in a two-dimensional square lattice tight-binding model with random flux. Localization lengths, fluctuations of the conductance, and the density of…

Mesoscale and Nanoscale Physics · Physics 2009-01-23 A. Furusaki

We analyze a mathematical model of elastic dislocations with applications to geophysics, where by an elastic dislocation we mean an open, oriented Lipschitz surface in the interior of an elastic solid, across which there is a discontinuity…

Analysis of PDEs · Mathematics 2019-11-13 Andrea Aspri , Elena Beretta , Anna L. Mazzucato , Maarten V. de Hoop

We study moderate deviations in the exponential corner growth model, both in the bulk setting and the increment-stationary setting. The main results are sharp right-tail bounds on the last-passage time and the exit point of the…

Probability · Mathematics 2020-04-10 Elnur Emrah , Chris Janjigian , Timo Seppäläinen

Lipschitz continuity is a crucial functional property of any predictive model, that naturally governs its robustness, generalisation, as well as adversarial vulnerability. Contrary to other works that focus on obtaining tighter bounds and…

Machine Learning · Computer Science 2024-05-16 Grigory Khromov , Sidak Pal Singh