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Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the additional geometric complexity renders the diffusion transition term inexpressible…

Machine Learning · Computer Science 2023-11-01 Aaron Lou , Minkai Xu , Stefano Ermon

This paper investigates score-based diffusion models when the underlying target distribution is concentrated on or near low-dimensional manifolds within the higher-dimensional space in which they formally reside, a common characteristic of…

Machine Learning · Computer Science 2025-01-03 Gen Li , Yuling Yan

We report on the transition between an Anderson localized regime and a conductive regime in a 1D scattering system with correlated disorder. We show experimentally that when long-range correlations, in the form of a power-law spectral…

Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Bi'an Du , Wei Hu , Renjie Liao

The Anderson transition in three dimensions in a randomly varying magnetic flux is investigated in detail by means of the transfer matrix method with high accuracy. Both, systems with and without an additional random scalar potential are…

Disordered Systems and Neural Networks · Physics 2009-10-31 T. Kawarabayashi , B. Kramer , T. Ohtsuki

We investigate the scaling properties of the two-dimensional (2D) Anderson model of localization with purely off-diagonal disorder (random hopping). Using the transfer-matrix method and finite-size scaling we compute the infinite-size…

Disordered Systems and Neural Networks · Physics 2015-06-24 Andrzej Eilmes , Rudolf A. Roemer

The analysis and design of advection-diffusion based molecular communication (MC) systems in cylindrical environments is of particular interest for applications such as micro-fluidics and targeted drug delivery in blood vessels. Therefore,…

Emerging Technologies · Computer Science 2020-07-06 Maximilian Schäfer , Wayan Wicke , Lukas Brand , Rudolf Rabenstein , Robert Schober

We consider the multi-particle Anderson model on the lattice with infinite range but sub-exponentially decaying interaction and show the Anderson localization consisting of the spectral exponential and the strong dynamical localization. In…

Mathematical Physics · Physics 2017-06-28 Trésor Ekanga

Strong localization of light in three-dimensional disordered dielectric systems remains challenging to establish because it requires extremely strong recurrent scattering, while the long-lived localized contribution can be weak and masked…

Optics · Physics 2026-05-26 Yevgen Grynko , Jens Förstner

The Anderson transitions in a random magnetic field in three dimensions are investigated numerically. The critical behavior near the transition point is analyzed in detail by means of the transfer matrix method with high accuracy for…

Disordered Systems and Neural Networks · Physics 2017-09-27 T. Kawarabayashi , B. Kramer , T. Ohtsuki

We present a microscopic derivation of self-consistent equations of Anderson localization in a disordered medium of finite size. The derivation leads to a renormalized, position-dependent diffusion coefficient. The position dependence of…

Disordered Systems and Neural Networks · Physics 2009-04-03 N. Cherroret , S. E. Skipetrov

Density-dependent diffusion is a widespread phenomenon in nature. We have examined the density-dependent diffusion behavior of some biological processes such as tumor growth and invasion [23]. Here, we extend our previous work by developing…

Statistical Mechanics · Physics 2022-02-14 Ahmed M. Fouad , Marwa M. Fouad

Modelling random dynamical systems in continuous time, diffusion processes are a powerful tool in many areas of science. Model parameters can be estimated from time-discretely observed processes using Markov chain Monte Carlo (MCMC) methods…

Computation · Statistics 2020-10-12 Susanne Pieschner , Christiane Fuchs

We consider systems of particles hopping stochastically on $d$-dimensional lattices with space-dependent probabilities. We map the master equation onto an evolution equation in a Fock space where the dynamics are given by a quantum…

Condensed Matter · Physics 2007-05-23 Gunter Schuetz , Sven Sandow

Anderson localisation -- the inhibition of wave propagation in disordered media -- is a surprising interference phenomenon which is particularly intriguing in two-dimensional (2D) systems. While an ideal, non-interacting 2D system of…

We investigate diffusion of excitation in one- and two-dimensional lattices with random on-site energies and deterministic long-range couplings (hopping) inversely proportional to the distance. Three regimes of diffusion are observed in…

Disordered Systems and Neural Networks · Physics 2020-11-18 Karol Kawa , Paweł Machnikowski

The localization lengths of long-range correlated disordered chains are studied for electronic wavefunctions in the Anderson model and for vibrational states. A scaling theory close to the band edge is developed in the Anderson model and…

Disordered Systems and Neural Networks · Physics 2009-11-07 Stefanie Russ

Dynamical localization phenomena of monochromatically perturbed standard map (SM) and Anderson map (AM), which are both identified with a two-dimensional disordered system under suitable conditions, are investigated by the numerical…

Disordered Systems and Neural Networks · Physics 2018-01-24 Hiroaki S. Yamada , Fumihiro Matsui , Kensuke S. Ikeda

We follow the dynamics of nonlinear waves in two-dimensional disordered lattices with tunable nonlinearity. In the absence of nonlinear terms Anderson localization traps the packet in space. For the nonlinear case a destruction of Anderson…

Chaotic Dynamics · Physics 2012-03-15 T. V. Laptyeva , J. D. Bodyfelt , S. Flach

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song…

Computer Vision and Pattern Recognition · Computer Science 2023-10-09 Simian Luo , Yiqin Tan , Longbo Huang , Jian Li , Hang Zhao