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Journal articles on the topic 'Stochastic dynamics'

To see the other types of publications on this topic, follow the link: Stochastic dynamics.

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

IMKELLER, PETER, and ADAM HUGH MONAHAN. "CONCEPTUAL STOCHASTIC CLIMATE MODELS." Stochastics and Dynamics 02, no. 03 (September 2002): 311–26. http://dx.doi.org/10.1142/s0219493702000443.

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From July 9 to 11, 2001, 50 researchers from the fields of climate dynamics and stochastic analysis met in Chorin, Germany, to discuss the idea of stochastic models of climate. The present issue of Stochastics and Dynamics collects several papers from this meeting. In this introduction to the volume, the idea of simple conceptual stochastic climate models is introduced amd recent results in the mathematically rigorous development and analysis of such models are reviewed. As well, a brief overview of the application of ideas from stochastic dynamics to simple models of the climate system is given.

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2

Ferrandis, Eduardo. "On the stochastic approach to marine population dynamics." Scientia Marina 71, no. 1 (March 30, 2007): 153–74. http://dx.doi.org/10.3989/scimar.2007.71n1153.

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3

Manolis, George D. "Stochastic soil dynamics." Soil Dynamics and Earthquake Engineering 22, no. 1 (January 2002): 3–15. http://dx.doi.org/10.1016/s0267-7261(01)00055-0.

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4

Cabrales, Antonio. "Stochastic Replicator Dynamics." International Economic Review 41, no. 2 (May 2000): 451–81. http://dx.doi.org/10.1111/1468-2354.00071.

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5

Mantelli, Elisa, Matteo Bernard Bertagni, and Luca Ridolfi. "Stochastic ice stream dynamics." Proceedings of the National Academy of Sciences 113, no. 32 (July 25, 2016): E4594—E4600. http://dx.doi.org/10.1073/pnas.1600362113.

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Ice streams are narrow corridors of fast-flowing ice that constitute the arterial drainage network of ice sheets. Therefore, changes in ice stream flow are key to understanding paleoclimate, sea level changes, and rapid disintegration of ice sheets during deglaciation. The dynamics of ice flow are tightly coupled to the climate system through atmospheric temperature and snow recharge, which are known exhibit stochastic variability. Here we focus on the interplay between stochastic climate forcing and ice stream temporal dynamics. Our work demonstrates that realistic climate fluctuations are able to (i) induce the coexistence of dynamic behaviors that would be incompatible in a purely deterministic system and (ii) drive ice stream flow away from the regime expected in a steady climate. We conclude that environmental noise appears to be crucial to interpreting the past behavior of ice sheets, as well as to predicting their future evolution.

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6

Ohzeki, Masayuki. "Stochastic gradient method with accelerated stochastic dynamics." Journal of Physics: Conference Series 699 (March 2016): 012019. http://dx.doi.org/10.1088/1742-6596/699/1/012019.

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7

Sopasakis, Alexandros. "Lattice Free Stochastic Dynamics." Communications in Computational Physics 12, no. 3 (September 2012): 691–702. http://dx.doi.org/10.4208/cicp.110211.200611a.

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AbstractWe introduce a lattice-free hard sphere exclusion stochastic process. The resulting stochastic rates are distance based instead of cell based. The corresponding Markov chain build for this many particle system is updated using an adaptation of the kinetic Monte Carlo method. It becomes quickly apparent that due to the lattice-free environment, and because of that alone, the dynamics behave differently than those in the lattice-based environment. This difference becomes increasingly larger with respect to particle densities/temperatures. The well-known packing problem and its solution (Palasti conjecture) seem to validate the resulting lattice-free dynamics.

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8

Landim, Claudio, Stefano Olla, and Herbert Spohn. "Large Scale Stochastic Dynamics." Oberwolfach Reports 10, no. 4 (2013): 3039–113. http://dx.doi.org/10.4171/owr/2013/52.

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9

Bodineau, Thierry, Fabio Toninelli, and Bálint Tóth. "Large Scale Stochastic Dynamics." Oberwolfach Reports 13, no. 4 (December 20, 2017): 3031–86. http://dx.doi.org/10.4171/owr/2016/54.

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10

Bodineau, Thierry, Fabio Toninelli, and Bálint Tóth. "Large Scale Stochastic Dynamics." Oberwolfach Reports 16, no. 3 (September 9, 2020): 2605–69. http://dx.doi.org/10.4171/owr/2019/42.

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11

MinPing, QIAN, and JIANG DaQuan. "Non-equilibrium stochastic dynamics." SCIENTIA SINICA Mathematica 47, no. 12 (December 1, 2017): 1703–16. http://dx.doi.org/10.1360/n012017-00178.

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12

den Hollander, F. "Metastability under stochastic dynamics." Stochastic Processes and their Applications 114, no. 1 (November 2004): 1–26. http://dx.doi.org/10.1016/j.spa.2004.07.007.

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13

MAJEWSKI, ADAM W., and BOGUSLAW ZEGARLINSKI. "QUANTUM STOCHASTIC DYNAMICS II." Reviews in Mathematical Physics 08, no. 05 (July 1996): 689–713. http://dx.doi.org/10.1142/s0129055x9600024x.

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We shortly review the progress in the domain of stochastic dynamics for quantum spin systems on a lattice. We also present some new results obtained in the framework of noncommutative [Formula: see text] spaces. In particular, using noncommutative Radon-Nikodym theorem of A. Connes we construct Markov generators of stochastic dynamics of spin flip type for systems at high temperatures or on one-dimensional lattice and with interactions of finite range at arbitrary temperatures.

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14

Foster, Dean, and Peyton Young. "Stochastic evolutionary game dynamics∗." Theoretical Population Biology 38, no. 2 (October 1990): 219–32. http://dx.doi.org/10.1016/0040-5809(90)90011-j.

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15

Restrepo, Juan M., and Shankar Venkataramani. "Stochastic longshore current dynamics." Advances in Water Resources 98 (December 2016): 186–97. http://dx.doi.org/10.1016/j.advwatres.2016.11.002.

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16

Caputo, Pietro, Fabio Toninelli, and Bálint Tóth. "Large Scale Stochastic Dynamics." Oberwolfach Reports 19, no. 3 (June 13, 2023): 2399–466. http://dx.doi.org/10.4171/owr/2022/41.

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17

Kopytov, P. D., I. D. Korolev, and O. A. Kulish. "Stochastic Dynamics of TEMPEST." LETI Transactions on Electrical Engineering & Computer Science 17, no. 1 (2024): 32–41. http://dx.doi.org/10.32603/2071-8985-2024-17-1-32-41.

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We set out to develop a stochastic model of TEMPEST in order to describe the behavior of electromagnetic radiation taking into account external effects. When investigating the stochastic dynamics of TEMPEST, the processes affecting the TEMPEST system in space and time are considered and simulated. This approach allows accurate consideration of random fluctuations and statistical characteristics when analyzing the channels of information leakage due to TEMPEST. The vulnerable criterion in the system security consists in the property of polarization as the main factor of uncertainty and unpredictability of the system in ideal experimental conditions.

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18

Wieczorek, Radosław. "Markov chain model of phytoplankton dynamics." International Journal of Applied Mathematics and Computer Science 20, no. 4 (December 1, 2010): 763–71. http://dx.doi.org/10.2478/v10006-010-0058-7.

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Markov chain model of phytoplankton dynamicsA discrete-time stochastic spatial model of plankton dynamics is given. We focus on aggregative behaviour of plankton cells. Our aim is to show the convergence of a microscopic, stochastic model to a macroscopic one, given by an evolution equation. Some numerical simulations are also presented.

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19

Yamamoto, Takehiro, Shinpei Hirota, and Takuya Fujiwara. "A Stochastic Rotation Dynamics Model for Dilute Spheroidal Colloid Suspensions." Nihon Reoroji Gakkaishi 44, no. 3 (2016): 185–88. http://dx.doi.org/10.1678/rheology.44.185.

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20

H. Hirpara, Ravish, and Shambhu N. Sharma. "On the Stochastic Filtering Theory of a Power System Dynamics." Transactions of the Institute of Systems, Control and Information Engineers 29, no. 1 (2016): 9–17. http://dx.doi.org/10.5687/iscie.29.9.

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21

Zhang, Yimin, Qiaoling Liu, and Bangchun Wen. "Dynamic Research of a Nonlinear Stochastic Vibratory Machine." Shock and Vibration 9, no. 6 (2002): 277–81. http://dx.doi.org/10.1155/2002/734102.

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This paper presents the dynamics problems of stochastic vibratory machine systems. The random responses of the vibratory machine systems with stochastic parameters subjected to random excitation are researched using a stochastic perturbation method. The numerical results are obtained. The dynamic characteristics of nonlinear stochastic vibratory machine are analyzed.

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22

Spanos, P. D., A. M. Chevallier, and N. P. Politis. "Nonlinear Stochastic Drill-String Vibrations." Journal of Vibration and Acoustics 124, no. 4 (September 20, 2002): 512–18. http://dx.doi.org/10.1115/1.1502669.

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Lateral vibrations of drill-strings used in oil well operations are considered. A finite elements based discretization procedure leads to a nonlinear dynamic system which is used to represent the drill-string inertia and stiffness characteristics, as well as the elasticity of the wall of the well. Due to the erratic pattern and the uncertainty of the forces at the drill-bit, a stochastic dynamics approach is adopted in investigating the problem. The method of statistical linearization is used, and expressions for determining an equivalent linear system to model the drill-string dynamics are derived. Further, a Monte Carlo simulation of the system dynamics is conducted by means of an Auto Regressive Moving Average (ARMA) digital filter, and by integrating the equations of motion using the Newmark scheme. Numerical results pertaining to data obtained by measurement while drilling (MWD) tools are presented. It is hoped that this study will enhance the interest in using stochastic dynamics techniques in drilling system analysis and design, as they can capture quite appropriately the inherent uncertainty of the bit forces and, potentially, of other sources.

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23

Wu, Yan-rui, Peng-fei Yang, and You-li Wu. "Stochastic Control-Oriented Modeling of Flexible Air-Breathing Hypersonic Vehicle." Mathematical Problems in Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/1648560.

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The flexible dynamics of commonly used air-breathing hypersonic vehicle model are not tractable for control design and the inevitable stochastic perturbations are usually neglected. Aiming at these deficiencies, reduced flexible dynamics are deducted in this paper and a stochastic control-oriented vehicle model is established accordingly. The responses of the original system to the deterministic and the stochastic part of the generalized force, which is treated as the input of the flexible dynamic system, are analyzed. After that, the simplified flexible dynamics is deduced to approximate the responses. The reduced flexible dynamics, which are tractable for control design since they greatly reduce the complexity of the original dynamics, are comprised of a simple function of the determined generalized force and an Ornstein-Uhlenbeck colored noise. Finally, the longitudinal dynamics in parametric strict feedback form are obtained by substituting the reduced flexible dynamics into the original model. The applicability of the simplified flexible dynamics is validated through the numerical simulations.

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24

Holm, Darryl D., and Tomasz M. Tyranowski. "Variational principles for stochastic soliton dynamics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2187 (March 2016): 20150827. http://dx.doi.org/10.1098/rspa.2015.0827.

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We develop a variational method of deriving stochastic partial differential equations whose solutions follow the flow of a stochastic vector field. As an example in one spatial dimension, we numerically simulate singular solutions (peakons) of the stochastically perturbed Camassa–Holm (CH) equation derived using this method. These numerical simulations show that peakon soliton solutions of the stochastically perturbed CH equation persist and provide an interesting laboratory for investigating the sensitivity and accuracy of adding stochasticity to finite dimensional solutions of stochastic partial differential equations. In particular, some choices of stochastic perturbations of the peakon dynamics by Wiener noise (canonical Hamiltonian stochastic deformations, CH-SD) allow peakons to interpenetrate and exchange order on the real line in overtaking collisions, although this behaviour does not occur for other choices of stochastic perturbations which preserve the Euler–Poincaré structure of the CH equation (parametric stochastic deformations, P-SD), and it also does not occur for peakon solutions of the unperturbed deterministic CH equation. The discussion raises issues about the science of stochastic deformations of finite-dimensional approximations of evolutionary partial differential equation and the sensitivity of the resulting solutions to the choices made in stochastic modelling.

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25

Holm, Darryl D. "Variational principles for stochastic fluid dynamics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2176 (April 2015): 20140963. http://dx.doi.org/10.1098/rspa.2014.0963.

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This paper derives stochastic partial differential equations (SPDEs) for fluid dynamics from a stochastic variational principle (SVP). The paper proceeds by taking variations in the SVP to derive stochastic Stratonovich fluid equations; writing their Itô representation; and then investigating the properties of these stochastic fluid models in comparison with each other, and with the corresponding deterministic fluid models. The circulation properties of the stochastic Stratonovich fluid equations are found to closely mimic those of the deterministic ideal fluid models. As with deterministic ideal flows, motion along the stochastic Stratonovich paths also preserves the helicity of the vortex field lines in incompressible stochastic flows. However, these Stratonovich properties are not apparent in the equivalent Itô representation, because they are disguised by the quadratic covariation drift term arising in the Stratonovich to Itô transformation. This term is a geometric generalization of the quadratic covariation drift term already found for scalar densities in Stratonovich's famous 1966 paper. The paper also derives motion equations for two examples of stochastic geophysical fluid dynamics; namely, the Euler–Boussinesq and quasi-geostropic approximations.

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26

Harrison, L. M., O. David, and K. J. Friston. "Stochastic models of neuronal dynamics." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 1075–91. http://dx.doi.org/10.1098/rstb.2005.1648.

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Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have already been established for deterministic systems. The potential importance of modelling density dynamics (as opposed to more conventional neural mass models) is that they include interactions among the moments of neuronal states (e.g. the mean depolarization may depend on the variance of synaptic currents through nonlinear mechanisms). Here, we formulate a population model, based on biologically informed model-neurons with spike-rate adaptation and synaptic dynamics. Neuronal sub-populations are coupled to form an observation model, with the aim of estimating and making inferences about coupling among sub-populations using real data. We approximate the time-dependent solution of the system using a bi-orthogonal set and first-order perturbation expansion. For didactic purposes, the model is developed first in the context of deterministic input, and then extended to include stochastic effects. The approach is demonstrated using synthetic data, where model parameters are identified using a Bayesian estimation scheme we have described previously.

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27

Tran, Ngoc-Khanh. "The Functional Stochastic Discount Factor." Quarterly Journal of Finance 09, no. 04 (December 2019): 1950013. http://dx.doi.org/10.1142/s2010139219500137.

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By assuming that the stochastic discount factor (SDF) [Formula: see text] is a proper but unspecified function of state variables [Formula: see text], we show that this function [Formula: see text] must solve a simple second-order linear differential equation specified by state variables’ risk-neutral dynamics. Therefore, this assumption determines the most general possible SDFs and associated preferences, that are consistent with the given risk-neutral state dynamics and interest rate. A consistent SDF then implies the corresponding state dynamics in the data-generating measure. Our approach offers novel flexibilities to extend several popular asset pricing frameworks: affine and quadratic interest rate models, as well as models built on linearity-generating processes. We illustrate the approach with an international asset pricing model in which (i) interest rate has an affine dynamic term structure and (ii) the forward premium puzzle is consistent with consumption-risk rationales; the two asset pricing features previously deemed conceptually incompatible.

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28

Shanahan, Linda, and Surajit Sen. "Dynamics of stochastic and nearly stochastic two-party competitions." Physica A: Statistical Mechanics and its Applications 390, no. 10 (May 2011): 1800–1810. http://dx.doi.org/10.1016/j.physa.2010.12.041.

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29

Zambrini, J. C. "Stochastic dynamics: A review of stochastic calculus of variations." International Journal of Theoretical Physics 24, no. 3 (March 1985): 277–327. http://dx.doi.org/10.1007/bf00669792.

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30

Ganikhodzhaev, R. N., K. A. Kurganov, M. A. Tadzhieva, and F. H. Haydarov. "Динамика квадратичных стохастических операторов типа Вольтерра, соответствующих странным турнирам." Владикавказский математический журнал 24, no. 1 (March 29, 2024): 85–99. http://dx.doi.org/10.46698/n9080-6847-9986-u.

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By studying the dynamics of these operators on the simplex, focusing on the presence of an interior fixed point, we investigate the conditions under which the operators exhibit nonergodic behavior. Through rigorous analysis and numerical simulations, we demonstrate that certain parameter regimes lead to nonergodicity, characterized by the convergence of initial distributions to a limited subset of the simplex. Our findings shed light on the intricate dynamics of quadratic stochastic operators with interior fixed points and provide insights into the emergence of nonergodic behavior in complex dynamical systems. Also, the nonergodicity of quadratic stochastic operators of Volterra type with an interior fixed point defined in a simplex introduces additional complexity to the already intricate dynamics of such systems. In this context, the presence of an interior fixed point within the simplex further complicates the exploration of the state space and convergence properties of the operator. In this paper, we give sufficiency and necessary conditions for the existence of strange tournaments. Also, we prove the nonergodicity of quadratic stochastic operators of Volterra type with an interior fixed point, defined in a simplex.

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31

Iwasa, Yoh, Franziska Michor, and Martin A. Nowak. "Stochastic Tunnels in Evolutionary Dynamics." Genetics 166, no. 3 (March 2004): 1571–79. http://dx.doi.org/10.1534/genetics.166.3.1571.

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32

Ushikubo, T., W. Inoue, M. Yoda, and M. Sasai. "3P287 Stochastic Dynamics of Repressilator." Seibutsu Butsuri 44, supplement (2004): S261. http://dx.doi.org/10.2142/biophys.44.s261_3.

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33

Dinner, Aaron R., Jonathan C. Mattingly, Jeremy O. B. Tempkin, Brian Van Koten, and Jonathan Weare. "Trajectory Stratification of Stochastic Dynamics." SIAM Review 60, no. 4 (January 2018): 909–38. http://dx.doi.org/10.1137/16m1104329.

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34

Peretto, Pierre, and Jean-jacques Niez. "Stochastic Dynamics of Neural Networks." IEEE Transactions on Systems, Man, and Cybernetics 16, no. 1 (January 1986): 73–83. http://dx.doi.org/10.1109/tsmc.1986.289283.

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35

Drozdov, A. N., and J. J. Brey. "Operator expansions in stochastic dynamics." Physical Review E 57, no. 2 (February 1, 1998): 1284–89. http://dx.doi.org/10.1103/physreve.57.1284.

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36

Silberschmidt, Vadim V. "Dynamics of Stochastic Damage Evolution." International Journal of Damage Mechanics 7, no. 1 (January 1998): 84–98. http://dx.doi.org/10.1177/105678959800700104.

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37

Keller, Matthias, and Günter Mahler. "Stochastic dynamics and quantum measurement." Quantum and Semiclassical Optics: Journal of the European Optical Society Part B 8, no. 1 (February 1996): 223–35. http://dx.doi.org/10.1088/1355-5111/8/1/016.

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38

Nakao, K. i., Y. Nambu, and M. Sasaki. "Stochastic Dynamics of New Inflation." Progress of Theoretical Physics 80, no. 6 (December 1, 1988): 1041–68. http://dx.doi.org/10.1143/ptp.80.1041.

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39

Foo, Jasmine, Kevin Leder, and Franziska Michor. "Stochastic dynamics of cancer initiation." Physical Biology 8, no. 1 (February 1, 2011): 015002. http://dx.doi.org/10.1088/1478-3975/8/1/015002.

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40

Dayan, Ido, Moshe Gitterman, and George H. Weiss. "Stochastic resonance in transient dynamics." Physical Review A 46, no. 2 (July 1, 1992): 757–61. http://dx.doi.org/10.1103/physreva.46.757.

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41

Saburov, Mansoor. "Dynamics of Double Stochastic Operators." Journal of Physics: Conference Series 697 (March 2016): 012014. http://dx.doi.org/10.1088/1742-6596/697/1/012014.

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42

Schueller, G. I., M. Shinozuka, and Ross B. Corotis. "Stochastic Methods in Structural Dynamics." Journal of Applied Mechanics 55, no. 2 (June 1, 1988): 501. http://dx.doi.org/10.1115/1.3173716.

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43

Farrell, Brian F., and Petros J. Ioannou. "Stochastic Dynamics of Baroclinic Waves." Journal of the Atmospheric Sciences 50, no. 24 (December 1993): 4044–57. http://dx.doi.org/10.1175/1520-0469(1993)050<4044:sdobw>2.0.co;2.

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44

Klotins, Eriks. "Stochastic Dynamics of Ferroelectric Polarization." Ferroelectrics 370, no. 1 (October 21, 2008): 184–95. http://dx.doi.org/10.1080/00150190802381548.

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45

Li, Jiejie, Laurent Blanchoin, and Christopher J. Staiger. "Signaling to Actin Stochastic Dynamics." Annual Review of Plant Biology 66, no. 1 (April 29, 2015): 415–40. http://dx.doi.org/10.1146/annurev-arplant-050213-040327.

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46

Constable, George W. A., Alan J. McKane, and Tim Rogers. "Stochastic dynamics on slow manifolds." Journal of Physics A: Mathematical and Theoretical 46, no. 29 (June 27, 2013): 295002. http://dx.doi.org/10.1088/1751-8113/46/29/295002.

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47

Harris, R. J., and G. M. Schütz. "Fluctuation theorems for stochastic dynamics." Journal of Statistical Mechanics: Theory and Experiment 2007, no. 07 (July 24, 2007): P07020. http://dx.doi.org/10.1088/1742-5468/2007/07/p07020.

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48

Guo, Boling, Yan Lv, and Xiaoping Yang. "Dynamics of stochastic Zakharov equations." Journal of Mathematical Physics 50, no. 5 (May 2009): 052703. http://dx.doi.org/10.1063/1.3131598.

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49

Sasa, Shin-ichi. "Collective dynamics from stochastic thermodynamics." New Journal of Physics 17, no. 4 (April 28, 2015): 045024. http://dx.doi.org/10.1088/1367-2630/17/4/045024.

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50

Biswas, A. "Dynamics of Stochastic Optical Solitons." Journal of Electromagnetic Waves and Applications 18, no. 2 (January 2004): 145–52. http://dx.doi.org/10.1163/156939304323062004.

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