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We develop a diffusion approximation for systems subject to fast random resetting by small amplitudes. Equivalently, this describes systems with frequent but small catastrophes. We demonstrate the validity of the approximation by computing…

Statistical Mechanics · Physics 2026-02-26 Tobias Galla

Density-density response functions are evaluated for nondegenerate multisubband electron systems in the random-phase approximation for arbitrary wave number and subband index. We consider both quasi-two-dimensional and quasi-one-…

Strongly Correlated Electrons · Physics 2009-10-31 Sviatoslav S. Sokolov , Nelson Studart

Diffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied to high-dimensional signals, including images and video,…

Machine Learning · Computer Science 2024-10-16 Nicholas M. Boffi , Arthur Jacot , Stephen Tu , Ingvar Ziemann

Curse of Dimensionality is an unavoidable challenge in statistical probability models, yet diffusion models seem to overcome this limitation, achieving impressive results in high-dimensional data generation. Diffusion models assume that…

Machine Learning · Statistics 2025-10-01 Zhenxin Zheng , Zhenjie Zheng

Tracer diffusion and hydrodynamic dispersion in two-dimensional fractures with self-affine roughness is studied by analytic and numerical methods. Numerical simulations were performed via the lattice-Boltzmann approach, using a new boundary…

Statistical Mechanics · Physics 2016-08-31 German Drazer , Joel Koplik

Particle transport, acceleration and energisation are phenomena of major importance for both space and laboratory plasmas. Despite years of study, an accurate theoretical description of these effects is still lacking. Validating models with…

Diffusion models perform remarkably well on high-dimensional data such as images, often using only a modest number of reverse-time steps. Despite this practical success, existing convergence theory does not fully explain why such samplers…

Machine Learning · Computer Science 2026-05-11 Ahmad Aghapour , Erhan Bayraktar

Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data…

Machine Learning · Computer Science 2023-05-02 Tangjun Wang , Zehao Dou , Chenglong Bao , Zuoqiang Shi

Diffusion of a particle passing over the saddle point of a two-dimensional quadratic potential is studied via a set of coupled Langevin equations and the expression for the passing probability is obtained exactly. The passing probability is…

Statistical Mechanics · Physics 2009-11-13 Chun-Yang Wang , Ying Jia , Jing-Dong Bao

In this work we include the elastic scattering of longitudinal electromagnetic waves in transport theory using a medium filled with point-like, electric dipoles. The interference between longitudinal and transverse waves creates two new…

Disordered Systems and Neural Networks · Physics 2021-05-17 B. A. van Tiggelen , S. E. Skipetrov

The comb model is a simplified description for anomalous diffusion under geometric constraints. It represents particles spreading out in a two-dimensional space where the motions in the x-direction are allowed only when the y coordinate of…

Computational Physics · Physics 2015-07-21 H. V. Ribeiro , A. A. Tateishi , L. G. A. Alves , R. S. Zola , E. K. Lenzi

Radiative and non-radiative charge carrier recombination in thin-film diodes plays a key role in determining the efficiency of electronic devices made of next generation semiconductors such as organic, perovskite and nanocrystals. In this…

Applied Physics · Physics 2019-08-06 Oskar J. Sandberg , Ardalan Armin

This is the second of a series of papers devoted to develop a microscopical approach to the dipole emission process and its relation to coherent transport in random media. In this Letter, we deduce a relation between the transverse decay…

Optics · Physics 2009-03-12 M. Donaire

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural…

Machine Learning · Statistics 2024-12-25 Antonio Sclocchi , Alessandro Favero , Matthieu Wyart

Herein an alternative model to reptation to describe concentrated polymer dynamics is developed. The model assumes that the chains act as blobs that are able to diffuse past each other in a compressed state. Allowing that the local…

Soft Condensed Matter · Physics 2022-02-25 Dave E. Dunstan

Intercellular signaling has an important role in organism development, but not all communication occurs using the same mechanism. Here, we analyze the energy efficiency of intercellular signaling by two canonical mechanisms: diffusion of…

Quantitative Methods · Quantitative Biology 2022-11-30 Hyunjoong Kim , Yoichiro Mori , Joshua B. Plotkin

The transport of individual entities through interconnected structures is a process of practical relevance both in biology and technology. Examples are given by diffusive dynamics of molecules in porous structures. In soft environments,…

Soft Condensed Matter · Physics 2026-02-09 Jakob Mihatsch , Andreas M. Menzel

In this work we investigate the process of pattern formation in a two dimensional domain for a reaction-diffusion system with nonlinear diffusion terms and the competitive Lotka-Volterra kinetics. The linear stability analysis shows that…

Pattern Formation and Solitons · Physics 2014-03-03 G. Gambino , M. C. Lombardo , M. Sammartino

Inspired by problems in biochemical kinetics, we study statistical properties of an overdamped Langevin process whose friction coefficient depends on the state of a similar, unobserved process. Integrating out the latter, we derive the long…

Statistical Mechanics · Physics 2009-08-13 Golan Bel , Ilya Nemenman

We study the real-time and real-space dynamics of charge in the one-dimensional Hubbard model in the limit of high temperatures. To this end, we prepare pure initial states with sharply peaked density profiles and calculate the time…

Strongly Correlated Electrons · Physics 2017-12-04 R. Steinigeweg , F. Jin , H. De Raedt , K. Michielsen , J. Gemmer
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