Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · cond-mat/0407074

Localization of transverse waves in randomly layered media at oblique incidence

K. Yu. Bliokh·V. D. Freilikher
View original source ↗Read source document ↗Export metadata ↓

Abstract

We investigate the oblique incidence of transverse waves on a randomly layered medium in the limit of strong disorder. An approximate method for calculating the inverse localization length based on the assumptions of zero energy flux and complete phase stochastization is presented. Two effects not found at normal incidence have been studied: dependence of the localization length on the polarization, and decrease of the localization length due to the internal reflections from layers with small refractive indexes. The inverse localization length (attenuation rate) for P-polarized radiation is shown to be always smaller than that of S-waves, which is to say that long enough randomly layered sample polarizes transmitted radiation. The localization length for P-polarization depends non-monotonically on the angle of propagation, and under certain conditions turns to infinity at some angle, which means that typical (non-resonant) random realizations become transparent at this angle of incidence (stochastic Brewster effect).

Explore related subjects

cond-mat.dis-nnphysics.optics

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K. Yu. Bliokh, V. D. Freilikher. 2004-10-20. Localization of transverse waves in randomly layered media at oblique incidence. https://doi.org/10.1103/physrevb.70.245121

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The critical slowing down in training diffusion models

Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently enabled major advances, their behavior remains poorly understood, with limited theoretical control over when and why they succeed. Here we provide such insight for diffusion models---a class of generative schemes highly effective in practice---by analyzing their application to the $O(n)$ model of statistical field theory in the Gaussian limit $n \to \infty$. In this analytically tractable setting, we show that training a score model with a one-layer network architecture matching the exact solution exhibits a form of critical slowing down in parameter learning. This slowing down also impacts the generation process, indicating that the well-known difficulties of sampling near criticality persist even for learned generative models. To overcome this bottleneck, we consider the power of architectural depth. We find that using a two-layer architecture drastically reduces the critical slowing down, with the training time scaling logarithmically rather than quadratically with system size. Using a Fourier implementation of the architecture, we further show that this acceleration in training time can be achieved without drastically increasing operational complexity. Taken together, these results demonstrate that diffusion models can overcome the critical slowing down through appropriate architectural design, and establish a controlled framework for understanding and improving learned sampling methods in statistical physics and beyond.

cond-mat.dis-nn↗
arXiv2026

Switching diffusivity selects Pareto tail exponent in random growth with redistribution

Random multiplicative growth with redistribution generates stationary Pareto wealth tails in the Bouchaud-Mézard model, but assumes a fixed multiplicative noise intensity. This is restrictive for physical and financial growth processes, where volatility (diffusivity) is often fluctuating. We replace the constant noise intensity by a switching diffusivity and ask how these fluctuations select the Pareto stationary tail. For a geometric Brownian motion with switching diffusivity, the long-time Gaussian limit holds when the redraw law has finite mean and variance. The asymptotic variance retains a contribution from diffusivity persistence. With redistribution and a general redraw law, the stationary large-wealth problem is characterized by a spectral condition for admissible algebraic modes. For a two-state diffusivity, an exact tail analysis gives a Pareto exponent interpolating between the high-diffusivity slow-refresh limit and the mean-diffusivity fast-refresh Bouchaud-Mézard limit.

cond-mat.dis-nn↗
arXiv2026

Signatures of Nonergodicity in Sparse Random Matrices

The prevalence of sparsity in the Fock space graph of interacting many-body systems motivates an investigation into the spectral statistics of sparse random matrices with on-site disorder. We numerically determine the delocalization-localization transition in the ground state as a function of the sparsity. The short-range energy correlation in the bulk indicates that the Anderson transition at infinite temperature occurs at the critical percolation limit of the sparse graph. By analytically deriving the energy moments and calculating the shifted kurtosis, we show that the critical sparsity threshold matches the Anderson transition. Furthermore, long-range energy correlations in the bulk spectrum reveal a Thouless energy scale, suggesting a broad nonergodic regime within the delocalized phase.

cond-mat.dis-nn↗