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Due to growing interest in quantum measurement, control and feedback, we reproduce a manuscript from 1992, presenting a simple physical and mathematical derivation of stochastic differential equations for wave functions of probed quantum…

Quantum Physics · Physics 2008-05-27 Yvan Castin , Jean Dalibard , Klaus Molmer

This paper extends the application of the stochastic variational method to noncentral interactions. Several examples are presented for three- and four-nucleon systems with realistic nuclear forces. The correlated Gaussians easily cope with…

Nuclear Theory · Physics 2009-10-30 K. Varga , Y. Ohbayasi , Y. Suzuki

We first introduce the concept of weak random periodic solutions of random dynamical systems. Then, we discuss the existence of such periodic solutions. Further, we introduce the definition of weak random periodic measures and study their…

Dynamical Systems · Mathematics 2022-08-03 Wei Sun , Zuo-Huan Zheng

Stochasticity is one of the most extensively researched topics in laboratory and space plasmas since it has been successful in explaining the various anomalous processes like transport, particle heating, particle loss etc. Since there is a…

Plasma Physics · Physics 2007-11-27 Ramesh Narayanan , Md. Nurujjaman , A. N. Sekar Iyengar

In this paper, we first analyze the strong and weak convergence of projective integration methods for multiscale stochastic dynamical systems driven by $\alpha$-stable processes, which are used to estimate the effect that the fast…

Probability · Mathematics 2020-06-02 Yanjie Zhang , Xiao Wang , Zibo Wang , Jinqiao Duan

This work presents an analysis of ocean wave data including rogue waves. A stochastic approach based on the theory of Markov processes is applied. With this analysis we achieve a characterization of the scale dependent complexity of ocean…

Data Analysis, Statistics and Probability · Physics 2015-06-18 A. Hadjihosseini , J. Peinke , N. P. Hoffmann

The periodic standing-wave method for binary inspiral computes the exact numerical solution for periodic binary motion with standing gravitational waves, and uses it as an approximation to slow binary inspiral with outgoing waves. Important…

General Relativity and Quantum Cosmology · Physics 2009-11-10 Zeferino Andrade , Christopher Beetle , Alexey Blinov , Benjamin Bromley , Lior M. Burko , Maria Cranor , Robert Owen , Richard H. Price

In this paper, random and stochastic processes are defined on fractal curves. Fractal calculus is used to define cumulative distribution function, probability density function, moments, variance and correlation function of stochastic…

General Mathematics · Mathematics 2024-03-18 Alireza Khalili Golmankhaneh , Kerri Welch , Cristina Serpa , Ivanka Stamova

At the macroscopic scale, many important models of collective motion fall into the class of kinematic flows for which both velocity and diffusion terms depend only on particle density. When total particle numbers are fixed and finite,…

Adaptation and Self-Organizing Systems · Physics 2022-04-12 Jeremy Worsfold , Tim Rogers , Paul Milewski

Convergence of stochastic processes with jumps to diffusion processes is investigated in the case when the limit process has discontinuous coefficients. An example is given in which the diffusion approximation of a queueing model yields a…

Probability · Mathematics 2016-09-07 N. V. Krylov , R. Liptser

In this work it is shown how the immersed boundary method of (Peskin2002) for modeling flexible structures immersed in a fluid can be extended to include thermal fluctuations. A stochastic numerical method is proposed which deals with…

Soft Condensed Matter · Physics 2023-02-28 P. J. Atzberger , P. R. Kramer , C. S. Peskin

We review opportunities for stochastic geometric mechanics to incorporate observed data into variational principles, in order to derive data-driven nonlinear dynamical models of effects on the variability of computationally resolvable…

Chaotic Dynamics · Physics 2018-06-28 François Gay-Balmaz , Darryl D. Holm

A multiscale analysis of 1D stochastic bistable reaction-diffusion equations with additive noise is carried out w.r.t. travelling waves within the variational approach to stochastic partial differential equations. It is shown with explicit…

Probability · Mathematics 2019-02-11 Jennifer Krüger , Wilhelm Stannat

The theory of slow manifolds is an important tool in the study of deterministic dynamical systems, giving a practical method by which to reduce the number of relevant degrees of freedom in a model, thereby often resulting in a considerable…

Statistical Mechanics · Physics 2013-07-01 George W A Constable , Alan J McKane , Tim Rogers

The status of stochastic cooling and developments over the years are reviewed with reference to much of the original work. Both theoretical and technological subjects are considered.

Accelerator Physics · Physics 2008-11-26 John Marriner

We introduce the stochastic band structure, a method giving the dispersion relation for waves propagating in periodic media or along waveguides, and subject to material loss or radiation damping. Instead of considering an explicit or…

Computational Physics · Physics 2023-06-30 Vincent Laude , Maria E. Korotyaeva

A new tool for modeling electrochemical kinetics is presented. An extension of the Stochastic Simulation Algorithm framework to electrochemical systems is proposed. The physical justifications and constraints for the derivation of a…

Chemical Physics · Physics 2016-09-20 O. Beruski

We present a concise outlook on future experimental tests of spontaneous wave function collapse models. We discuss tests based on force noise measurements in ultrasensitive mechanical systems, spontaneous heating in ultracold solid matter…

Quantum Physics · Physics 2021-04-14 Andrea Vinante , Hendrik Ulbricht

Based on the existing literature, this article presents the different ways of choosing the parameters of stochastic volatility models in general, in the context of pricing financial derivative contracts. This includes the use of stochastic…

Pricing of Securities · Quantitative Finance 2025-12-24 Fabien Le Floc'h

A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear partial differential equations for the probability density…

Machine Learning · Computer Science 2023-12-19 Tyler E. Maltba , Vishwas Rao , Daniel Adrian Maldonado