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The smoothing distribution is the conditional distribution of the diffusion process in the space of trajectories given noisy observations made continuously in time. It is generally difficult to sample from this distribution. We use the…

Probability · Mathematics 2025-03-07 Oskar Eklund , Annika Lang , Moritz Schauer

We construct and study branching Markov processes on the space of finite configurations of the state space of a given standard process, controlled by a branching kernel and a killing one. In particular, we may start with a superprocess,…

Probability · Mathematics 2015-08-03 Lucian Beznea , Oana Lupascu

In this letter we describe a new micro-bunching instability occurring in charged particle beams propagating along a straight trajectory: based on the dynamics we named it a Plasma Cascade Instability.

Accelerator Physics · Physics 2021-02-03 Vladimir N. Litvinenko , Gang Wang , Yichao Jing , Dmitry Kayran , Jun Ma , Irina Petrushina , Igor Pinayev , Kai Shih

Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilistic inference and learning under generative models with…

Machine Learning · Computer Science 2024-02-28 Prakhar Verma , Vincent Adam , Arno Solin

We study a supposed model for branched polymers which was shown in two dimensions to be in the universality class of ordinary percolation. We confirm this by high statistics simulations and show that it is in the percolation universality…

Statistical Mechanics · Physics 2007-05-23 Peter Grassberger

Complex systems are composed of many particles or agents that move and interact with one another. The underlying mathematical framework to model many of these systems must incorporate the spatial transport of particles and their…

Statistical Mechanics · Physics 2026-02-09 Mauricio J. del Razo , Tommaso Lamma , Wout Merbis

We consider a collection of weakly interacting diffusion processes moving in a two-scale locally periodic environment. We study the large deviations principle of the empirical distribution of the particles' positions in the combined limit…

Probability · Mathematics 2022-11-03 Zachary Bezemek , Konstantinos Spiliopoulos

A two-type continuous-state branching process in varying environments is constructed as the pathwise unique solution of a system of stochastic equations driven by time-space noises, where the pathwise uniqueness is derived from a comparison…

Probability · Mathematics 2025-02-07 Zenghu Li , Junyan Zhang

This book covers a wide range of problems involving the applications of stochastic processes, stochastic calculus, large deviation theory, group representation theory and quantum statistics to diverse fields in dynamical systems,…

Mathematical Physics · Physics 2021-08-13 Harish Parthasarathy

The effect of a stochastic displacement field on a statistically independent point process is analyzed. Stochastic displacement fields can be divided into two large classes: spatially correlated and uncorrelated. For both cases exact…

Statistical Mechanics · Physics 2008-11-26 Andrea Gabrielli

Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by studying the structure that they take in 3D space. However,…

Biomolecules · Quantitative Biology 2023-02-27 Gabriele Corso

We provide an explicit rigorous derivation of a diffusion limit - a stochastic differential equation with additive noise - from a deterministic skew-product flow. This flow is assumed to exhibit time-scale separation and has the form of a…

Dynamical Systems · Mathematics 2015-05-27 I. Melbourne , A. M. Stuart

A general class of non-Markov, supercritical Gaussian branching particle systems is introduced and its long-time asymptotics is studied. Both weak and strong laws of large numbers are developed with the limit object being characterized in…

Probability · Mathematics 2018-07-30 Michael A. Kouritzin , Khoa Lê , Deniz Sezer

Information percolation is a new method for analyzing stochastic spin systems through classifying and controlling the clusters of information-flow in the space-time slab. It yielded sharp mixing estimates (cutoff with an $O(1)$-window) for…

Probability · Mathematics 2015-01-05 Eyal Lubetzky , Allan Sly

In this paper we present theory of novel micro-bunching instability. We named it Plasma-Cascade Instability

Plasma Physics · Physics 2021-03-03 Vladimir N. Litvinenko , Gang Wang

A numerical method is devised for study of stochastic partial differential equations describing directed percolation, the contact process, and other models with a continuous transition to an absorbing state. Owing to the heightened…

Condensed Matter · Physics 2009-10-22 Ronald Dickman

We propose a stochastic branching particle-based method for solving nonlinear non-conservative advection-diffusion-reaction equations. The method splits the evolution into an advection-diffusion step, based on a linearized Kolmogorov…

Numerical Analysis · Mathematics 2025-12-02 Liyao Lyu , Huan Lei

Fractional calculus allows one to generalize the linear, one-dimensional, diffusion equation by replacing either the first time derivative or the second space derivative by a derivative of fractional order. The fundamental solutions of…

Statistical Mechanics · Physics 2007-05-23 Francesco Mainardi , Paolo Paradisi , Rudolf Gorenflo

We investigate a class of stochastic fragmentation processes involving stable and unstable fragments. We solve analytically for the fragment length density and find that a generic algebraic divergence characterizes its small-size tail.…

Statistical Mechanics · Physics 2007-05-23 P. L. Krapivsky , E. Ben-Naim , I. Grosse

Stochastic processes are proposed whose master equations coincide with classical wave, telegraph, and Klein-Gordon equations. Similar to predecessors based on the Goldstein-Kac telegraph process, the model describes the motion of particles…

Statistical Mechanics · Physics 2015-05-18 A. V. Plyukhin