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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Stochastics dynamics"

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

Yoda, M., and M. Sasai. "2P308 Stochastics dynamics of coupled repressilators." Seibutsu Butsuri 45, supplement (2005): S196. http://dx.doi.org/10.2142/biophys.45.s196_4.

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3

de Levie, Robert. "Stochastics, the Basis of Chemical Dynamics." Journal of Chemical Education 77, no. 6 (June 2000): 771. http://dx.doi.org/10.1021/ed077p771.

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4

Lin, Winston T., Hong-Jen Lin, and Yueh H. Chen. "The Dynamics and Stochastics of Currency Betas Based on the Unbiasedness Hypothesis in Foreign Exchange Markets." Multinational Finance Journal 6, no. 3/4 (December 1, 2002): 167–95. http://dx.doi.org/10.17578/6-3/4-2.

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5

Ovchinnikov, Igor V., Wenyuan Li, Yuquan Sun, Andrew E. Hudson, Karlheinz Meier, Robert N. Schwartz, and Kang L. Wang. "Criticality or Supersymmetry Breaking?" Symmetry 12, no. 5 (May 12, 2020): 805. http://dx.doi.org/10.3390/sym12050805.

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In many stochastic dynamical systems, ordinary chaotic behavior is preceded by a full-dimensional phase that exhibits 1/f-type power spectra and/or scale-free statistics of (anti)instantons such as neuroavalanches, earthquakes, etc. In contrast with the phenomenological concept of self-organized criticality, the recently found approximation-free supersymmetric theory of stochastics (STS) identifies this phase as the noise-induced chaos (N-phase), i.e., the phase where the topological supersymmetry pertaining to all stochastic dynamical systems is broken spontaneously by the condensation of the noise-induced (anti)instantons. Here, we support this picture in the context of neurodynamics. We study a 1D chain of neuron-like elements and find that the dynamics in the N-phase is indeed featured by positive stochastic Lyapunov exponents and dominated by (anti)instantonic processes of (creation) annihilation of kinks and antikinks, which can be viewed as predecessors of boundaries of neuroavalanches. We also construct the phase diagram of emulated stochastic neurodynamics on Spikey neuromorphic hardware and demonstrate that the width of the N-phase vanishes in the deterministic limit in accordance with STS. As a first result of the application of STS to neurodynamics comes the conclusion that a conscious brain can reside only in the N-phase.
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6

Wissel, Christian. "Metastability, a consequence of stochastics in multiple stable population dynamics." Theoretical Population Biology 36, no. 3 (December 1989): 296–310. http://dx.doi.org/10.1016/0040-5809(89)90036-1.

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7

Синенко, D. Sinenko, Гараева, G. Garaeva, Еськов, Valeriy Eskov, Ворошилова, and A. Voroshilova. "Stochastic and chaos in evaluation order parameter in regenerative medicine." Complexity. Mind. Postnonclassic 3, no. 4 (July 10, 2014): 87–100. http://dx.doi.org/10.12737/7655.

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The basis of the third global paradigm of theory of chaos and self-organization, which focuses on the assessment of the chaotic dynamics of the state vector of complex biological systems using multi-dimensional phase space of states. The paper presents a comparative description of the effectiveness of the traditional stochastic methods and methods of calculating the parameters of quasi-attractors. It is showed the difference in efficiency (low) of stochastics, which leads to the uncertainty of the 1st kind, and methods of multidimensional phase spaces, providing the solution of system synthesis. Volumes quasi-attractors with kinesotherapy in patients with acute stroke increased 5.3 times in the initial stage of treatment, and then falling off sharply. It is discussed the need for parallel applications and stochastic methods and methods of theory of chaos and self-organization in the study of complex medical and biological systems.
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8

van Mourik, Anke M., Andreas Daffertshofer, and Peter J. Beek. "Extracting Global and Local Dynamics From the Stochastics of Rhythmic Forearm Movements." Journal of Motor Behavior 40, no. 3 (May 2008): 214–31. http://dx.doi.org/10.3200/jmbr.40.3.214-231.

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9

Горбунов, D. Gorbunov, Эльман, Kseniya Elman, Гавриленко, T. Gavrilenko, Григоренко, and V. Grigorenko. "Features stochastics and chaos theory processing myogram." Complexity. Mind. Postnonclassic 4, no. 1 (August 23, 2015): 45–53. http://dx.doi.org/10.12737/10864.

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In studies using the method of multi-dimensional phase space. In the study and modeling of complex biological objects (complexity) there is the possibility of introducing traditional physical methods in biological research and new methods based on chaos theory, self-organization. The paper shows the feasibility of applying the method of multi-dimensional phase space as a quantitative measure for the evaluation of chaotic dynamics on the example of the muscles (flexor of the little finger). As a measure of the state of the neuromuscular system of the person (weak muscle tension and strong, almost the maximum force) used quasi-attractors volumes of multidimensional phase space. This enables identification of the actual measurements of the parameters of the functional state with weak muscles (p = 5th Dan) and strong (P = 10 daN) static stress. Was built timebase signal obtained with myograph and were built autocorrelation function A (t) signal. In the end analysis of the biomechanical system based on a comparison of volume quasi-attractor, as well as on the basis of analysis of the Shannon entropy N. kzvaziattraktora volume displacement at low load is slightly less than the same amount of displacement under a heavy load of flexor muscles of the little finger, just as the values of the Shannon entropy at a heavy load is increased as compared with the values obtained by the weak muscle load.
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Еськов, V. Eskov, Джумагалиева, L. Dzhumagalieva, Еськов, Valeriy Eskov, Гудкова, and S. Gudkova. "Medicine and the Chaos Theory in Description of Individual and Particular." Journal of New Medical Technologies 21, no. 3 (September 5, 2014): 27–35. http://dx.doi.org/10.12737/5892.

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The article presents three approaches (deterministic, stochastic and chaotic – self-organizing) for studying biomedical systems. The authors show that complex biosystems cann’t be described by deterministic and stochastics because of constant changing parameters xi of a state vector of such systems x=x(t). The fundamental distinguish of deterministic and stochastic systems from chaotic – self-organizing is continuous movement x(t) in phase space of states. The authors also present complex of objects which the authors have been studying for the last 30 years and which conform the type III systems. The particular features of the personalized medicine are presented, that denies possibility of identification of body state at one measurement (a point in a phase space). It is connected with the fact that there is a uniform distribution x(t) in time-domain xi which is revealed in continuous change of distribution functions f(x) for different discrete recording time-domain x(t) at all xi. The authors assert that behavior dynamics of neural networks is similar to work of neuroemulators that is terminated by certainty in quasi-attractor’s volumes.
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Дисертації з теми "Stochastics dynamics"

1

Makuch, Martin. "Circumplanetary dust dynamics application to Martian dust tori and Enceladus dust plumes /." Phd thesis, [S.l.] : [s.n.], 2007. http://opus.kobv.de/ubp/volltexte/2007/1440.

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Mueller, Felix. "Formation of spatio–temporal patterns in stochastic nonlinear systems." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16527.

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Die vorliegende Arbeit befasst sich mit einer Reihe von Fragestellungen, die Forschungsfeldern wie rauschinduziertem Verhalten, Strukturbildung in aktiven Medien und Synchronisation nichlinearer Oszillatoren erwachsen. Die verwendeten nichtlinearen Modelle verfügen über erregbare, oszillatorische und bistabile Eigenschaften. Zusätzliche stochastische Fluktuationen tragen wesentlich zur Entstehung komplexer Dynamik bei. Modelliert wird, auf welche Weise sich extrazelluläre Kaliumkonzentration, gespeist von umliegenden Neuronen, auf die Aktivität dieser Neuronen auswirkt. Neben lokaler Dynamik wird die Ausbildung ausgedehnter Strukturen in einem heterogenem Medium analysiert. Die raum-zeitlichen Muster umfassen sowohl Wellenfronten und Spiralen als auch ungewöhnliche Strukturen, wie wandernde Cluster oder invertierte Wellen. Eine wesentliche Rolle bei der Ausprägung solcher Strukturen spielen die Randbedingungen des Systems. Sowohl für diskret gekoppelte bistabile Elemente als auch für kontinuierliche Fronten werden Methoden zur Berechnung von Frontgeschwindigkeiten bei fixierten Rändern vorgestellt. Typische Bifurkationen werden quantifiziert und diskutiert. Der Rückkopplungsmechanismus aus dem Modell neuronaler Einheiten und deren passiver Umgebung kann weiter abstrahiert werden. Ein Zweizustandsmodell wird über zwei Wartezeitverteilungen definiert, welche erregbares Verhalten widerspiegeln. Untersucht wird die instantane und die zeitverzögerte Antwort des Ensembles auf die Rückkopplung. Im Fall von Zeitverzögerung tritt eine Hopf-Bifurkation auf, die zu Oszillationen der mittleren Gesamtaktivität führt. Das letzte Kapitel befasst sich mit Diffusion und Transport von Brownschen Teilchen in einem raum-zeiltich periodischen Potential. Wieder sind es Synchronisationsmechanismen, die nahezu streuungsfreien Transport ermöglichen können. Für eine erhöhte effektiven Diffusion gelangen wir zu einer Abschätzung der maximierenden Parameter.
In this work problems are investigated that arises from resarch fields of noise induced dynamics, pattern formation in active media and synchronisation of self-sustained oscillators. The applied model systems exhibit excitable, oscillatory and bistable behavior as basic modes of nonlinear dynamics. Addition of stochastic fluctuations contribute to the appearance of complex behavior. The extracellular potassium concentration fed by surrounding activated neurons and the feeback to these neurons is modelled. Beside considering the local behavior, nucleation of spatially extended structures is studied. We find typical fronts and spirales as well as unusal patterns such as moving clusters and inverted waves. The boundary conditions of the considered system play an essential role in the formation process of such structures. We present methods to find expressions of the front velocity for discretely coupled bistable units as well as for the countinus front interacting with boundary values. Canonical bifurcation scenarios can be quantified. The feedback mechanism from the model for neuronal units can be generalized further. A two-state model is defined by two waiting time distributions representing excitable dynamics. We analyse the instantaneous and delayed response of the ensemble. In the case of delayed feedback a Hopf-bifurcation occur which lead to oscillations of the mean activity. In the last chapter the transport and diffusion of Brownian particles in a spatio-temporal oscillating potential is discussed. As a cause of nearly dispersionless transport synchronisations mechanisms can be identified. We find an estimation for parameter values which maximizes the effective diiffusion.
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3

Dean, David Stanley. "Stochastic dynamics." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318048.

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4

Almada, Monter Sergio Angel. "Scaling limit for the diffusion exit problem." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39518.

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A stochastic differential equation with vanishing martingale term is studied. Specifically, given a domain D, the asymptotic scaling properties of both the exit time from the domain and the exit distribution are considered under the additional (non-standard) hypothesis that the initial condition also has a scaling limit. Methods from dynamical systems are applied to get more complete estimates than the ones obtained by the probabilistic large deviation theory. Two situations are completely analyzed. When there is a unique critical saddle point of the deterministic system (the system without random effects), and when the unperturbed system escapes the domain D in finite time. Applications to these results are in order. In particular, the study of 2-dimensional heteroclinic networks is closed with these results and shows the existence of possible asymmetries. Also, 1-dimensional diffusions conditioned to rare events are further studied using these results as building blocks. The approach tries to mimic the well known linear situation. The original equation is smoothly transformed into a very specific non-linear equation that is treated as a singular perturbation of the original equation. The transformation provides a classification to all 2-dimensional systems with initial conditions close to a saddle point of the flow generated by the drift vector field. The proof then proceeds by estimates that propagate the small noise nature of the system through the non-linearity. Some proofs are based on geometrical arguments and stochastic pathwise expansions in noise intensity series.
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5

Lythe, Grant David. "Stochastic slow-fast dynamics." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338108.

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6

Restrepo, Juan M., and Shankar Venkataramani. "Stochastic longshore current dynamics." ELSEVIER SCI LTD, 2016. http://hdl.handle.net/10150/621938.

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We develop a stochastic parametrization, based on a 'simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike deterministic models, stochastic parameterization incorporates randomness and hence can only match the observations in a statistical sense. Unlike statistical emulators, in which the model is tuned to the statistical structure of the observation, stochastic parametrization are not directly tuned to match the statistics of the observations. Rather, stochastic parameterization combines deterministic, i.e physics based models with stochastic models for the "missing physics" to create hybrid models, that are stochastic, but yet can be used for making predictions, especially in the context of data assimilation. We introduce a novel measure of the utility of stochastic models of complex processes, that we call consistency of sensitivity. A model with poor consistency of sensitivity requires a great deal of tuning of parameters and has a very narrow range of realistic parameters leading to outcomes consistent with a reasonable spectrum of physical outcomes. We apply this metric to our stochastic parametrization and show that, the loss of certainty inherent in model due to its stochastic nature is offset by the model's resulting consistency of sensitivity. In particular, the stochastic model still retains the forward sensitivity of the deterministic model and hence respects important structural/physical constraints, yet has a broader range of parameters capable of producing outcomes consistent with the field data used in evaluating the model. This leads to an expanded range of model applicability. We show, in the context of data assimilation, the stochastic parametrization of longshore currents achieves good results in capturing the statistics of observation that were not used in tuning the model.
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7

Sanyal, Suman. "Stochastic dynamic equations." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Sanyal_09007dcc80519030.pdf.

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Thesis (Ph. D.)--Missouri University of Science and Technology, 2008.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed August 21, 2008) Includes bibliographical references (p. 124-131).
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8

Yilmaz, Bulent. "Stochastic Approach To Fusion Dynamics." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608517/index.pdf.

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This doctoral study consists of two parts. In the first part, the quantum statistical effects on the formation process of the heavy ion fusion reactions have been investigated by using the c-number quantum Langevin equation approach. It has been shown that the quantum effects enhance the over-passing probability at low temperatures. In the second part, we have developed a simulation technique for the quantum noises which can be approximated by two-term exponential colored noise.
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9

De, Fabritiis Gianni. "Stochastic dynamics of mesoscopic fluids." Thesis, Queen Mary, University of London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268402.

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Stocks, Nigel Geoffrey. "Experiments in stochastic nonlinear dynamics." Thesis, Lancaster University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315224.

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Книги з теми "Stochastics dynamics"

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D, Elworthy K., and Zambrini Jean-Claude, eds. Stochastic analysis, path integration, and dynamics: Emanations from "Summer Stochastics", Warwick 1987. Harlow, Essex, England: Longman Scientific & Technical, 1989.

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2

D, Elsworthy K., and Zambrini J. -C, eds. Stochastic analysis, path integration and dynamics: Emanations from "Summer stochastics", Warwick 1987. Harlow: Longman Scientific & Technical, 1989.

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3

Celledoni, Elena, Giulia Di Nunno, Kurusch Ebrahimi-Fard, and Hans Zanna Munthe-Kaas, eds. Computation and Combinatorics in Dynamics, Stochastics and Control. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0.

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4

Peter, Grabner, and Woess Wolfgang 1954, eds. Fractals in Graz 2001: Analysis-dynamics-geometry-stochastics. Boston, MA: Birkhäuser, 2002.

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5

S, Keane M., Denteneer Dee, Hollander F. den, and Verbitskiy Evgeny, eds. Dynamics & stochastics: Festschrift in Honour of M.S. Keane. Beachwood, Ohio: Institute of Mathematical Statistics, 2006.

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6

Grabner, Peter. Fractals in Graz 2001: Analysis -- Dynamics -- Geometry -- Stochastics. Basel: Birkhäuser Basel, 2003.

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7

Albeverio, S., Ph Blanchard, and D. Testard, eds. Stochastics, Algebra and Analysis in Classical and Quantum Dynamics. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-011-7976-8.

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8

Manolis, G. D. Stochastic structural dynamics in earthquake engineering. Southampton: WITPress, 2001.

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9

1943-, Anishchenko V. S., ed. Nonlinear dynamics of chaotic and stochastic systems: Tutorial and modern developments. Berlin: Springer, 2002.

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10

1958-, Sokolov Igor M., ed. Statistical thermodynamics and stochastic theory of nonequilibrium systems. Hackensack, NJ: World Scientific, 2005.

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Частини книг з теми "Stochastics dynamics"

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Jacobs, Konrad. "Markovian Dynamics." In Discrete Stochastics, 19–43. Basel: Birkhäuser Basel, 1992. http://dx.doi.org/10.1007/978-3-0348-8645-1_2.

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Jorgensen, Palle E. T., and James Tian. "Stochastics and Dynamics of Fractals." In Recent Developments in Operator Theory, Mathematical Physics and Complex Analysis, 171–216. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21460-8_5.

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3

Urbański, Mariusz, and Alexander Volberg. "A Rigidity Theorem in Complex Dynamics." In Fractal Geometry and Stochastics, 179–87. Basel: Birkhäuser Basel, 1995. http://dx.doi.org/10.1007/978-3-0348-7755-8_9.

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4

Mosco, Umberto. "Lagrangian Metrics and Fractal Dynamics." In Fractal Geometry and Stochastics II, 269–83. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8380-1_13.

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5

Gubinelli, Massimiliano. "Infinite Dimensional Rough Dynamics." In Computation and Combinatorics in Dynamics, Stochastics and Control, 401–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0_14.

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Baik, Jinho, Guillaume Barraquand, Ivan Corwin, and Toufic Suidan. "Facilitated Exclusion Process." In Computation and Combinatorics in Dynamics, Stochastics and Control, 1–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0_1.

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7

Ebrahimi-Fard, Kurusch, and W. Steven Gray. "The Faà di Bruno Hopf Algebra for Multivariable Feedback Recursions in the Center Problem for Higher Order Abel Equations." In Computation and Combinatorics in Dynamics, Stochastics and Control, 265–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0_10.

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Benth, Fred Espen, and André Süss. "Continuous-Time Autoregressive Moving-Average Processes in Hilbert Space." In Computation and Combinatorics in Dynamics, Stochastics and Control, 297–320. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0_11.

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Fløystad, Gunnar, and Hans Munthe-Kaas. "Pre- and Post-Lie Algebras: The Algebro-Geometric View." In Computation and Combinatorics in Dynamics, Stochastics and Control, 321–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0_12.

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Foissy, Loïc. "Extension of the Product of a Post-Lie Algebra and Application to the SISO Feedback Transformation Group." In Computation and Combinatorics in Dynamics, Stochastics and Control, 369–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0_13.

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Тези доповідей конференцій з теми "Stochastics dynamics"

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Ebeling, Werner, and Udo Erdmann. "Dynamics and stochastics of swarms of self-propelled Brownian particles." In SPIE's First International Symposium on Fluctuations and Noise, edited by Sergey M. Bezrukov, Hans Frauenfelder, and Frank Moss. SPIE, 2003. http://dx.doi.org/10.1117/12.501844.

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Ricks, Trenton, Thomas Lacy, Brett Bednarcyk, and Steven Arnold. "A Multiscale Modeling Methodology for Metal Matrix Composites Including Fiber Strength Stochastics." In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
20th AIAA/ASME/AHS Adaptive Structures Conference
14th AIAA. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-1965.

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Kareem, Ahsan, Fei Ding, and Jiawei Wan. "Aerodynamic Shape Tailoring of Buildings: A Fusion of CFD, Stochastics, Machine Learning and Beyond." In IABSE Congress, New York, New York 2019: The Evolving Metropolis. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/newyork.2019.0345.

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<p>Tall buildings exposed to wind undergo complex interactions, which precludes a functional relationship between wind and ist load effects. Accordingly, wind tunnels have traditionally served as a means of quantifying wind loads. In digital age with burgeoning growth in computational resources and parallel computing advances in computational fluid dynamics, computational simulations are evolving with a promise of becoming versatile, convenient and reliable means of assessing wind load effects. The major challenge to such an initiative has been the wind field around the structures marked by separated flows, which requires high fidelity simulation schemes to capture extreme loads, thus placing a high demand on computational resources. The emerging trend is to use a combination of CFD, stochastic emulation and machine learning approaches to overcome some of these challenges.</p><p>This paper will utilize this digital simulation approach to mitigate motion of tall buildings through shape morphing. It will illustrate a practical example involving shape optimization of buildings. To go beyond static optimization to mitigate wind effects, a brief overview of the fusion of sensing, computations and actuation in a cyberphysical space to autonomously morph structures to adaptively undergo shape changes in response to changes in coming wind conditions will follow.</p>
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4

Engel, Joachim. "Stochastic modeling and statistical thinking in technology supported environments." In Statistics and the Internet. International Association for Statistical Education, 2003. http://dx.doi.org/10.52041/srap.03303.

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To explore the role of technology in students modeling attempts we examined the efficacy of a Five-Steps-Program in a third-year statistics class for students preparing to be teachers: 1. Introduction of a “real-world” problem involving some aspects of data analysis; Activity or experiment to experience the dynamics of the phenomenon of interest; 2. Constructing a simulation model representing an aspect of the problem. Representing the simulation model in a technological environment (Lisp-Stat, Fathom); 3. Collecting and analyzing simulated data; Derivation of a simulation-based result; 4. Critically evaluating the simulation-based result and its conclusion; Validating the model by reflecting on the impact of the assumptions in the modeling step (sensitivity analysis, what-if scenarios) considering the role of stated assumptions, limitations of the model and possibly refining the model; how much do the conclusion depend on the assumptions? How do results change after slight deviations from these assumptions? 5. Mathematical analysis, arguments and proof involving concepts of probability and mathematical statistics. The topics included capture-recapture models, patterns in coin flipping sequences, tests based on runs, rencontre problem, coupon collector, randomized response techniques, regression and curve fitting. Each student prepared a topic and led a class session following above five- step plan. In the preceding semester all participants attended an introductory stochastics class.
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5

Zadorozhnyi, Vladimir N., Evgeniy B. Yudin, and Maria N. Yudina. "Graphs with complex stochastic increments." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239525.

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6

Gontchar, Igor I., Maria V. Chushnyakova, Vera K. Volkova, and Alexander I. Blesman. "Modeling a two-dimensional distorted stochastic harmonic oscillator." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239454.

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7

Champagnat, Nicolas, and Amaury Lambert. "Adaptive dynamics in logistic branching populations." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-14.

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8

Malysheva, Nadezhda N., and Aleksandr A. Pavlov. "Determination of probabilistic descriptions and stochastic processes of changes loads." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819045.

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Malysheva, Nadezhda N., and Aleksandr A. Pavlov. "Determination of probabilistic descriptions and stochastic processes of changes loads." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239485.

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10

Bertram, C. D. "Chaotic and mode-locked interactions between flow-induced collapsible-tube oscillation and pulsatile upstream forcing." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1303765.

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Звіти організацій з теми "Stochastics dynamics"

1

Burton, Robert M., and Jr. Topics in Stochastics, Symbolic Dynamics and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada336426.

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2

Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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3

HOLM, D. D., A. ACEVES, J. S. ALLEN, and ET AL. APPLIED NONLINEAR STOCHASTIC DYNAMICS. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/785030.

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4

Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, April 2021. http://dx.doi.org/10.46337/esdi.210414.

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Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of novel theoretical insights, methodological developments and geophysical applications across interdisciplinary mathematical, geophysical and information technological approaches towards a cross-cutting, mathematically sound, physically consistent, socially conscious and operationally effective Earth System Dynamic Intelligence. Going beyond the well established stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid techniques, ESDI paves the way to exploratory and disruptive developments along emerging information physical intelligence pathways, and bridges fundamental and operational complex problem solving across frontier natural, social and technical geosciences. Overall, the ESDI Flagship breeds a nascent field and community where methodological ingenuity and natural process understanding come together to shed light onto fundamental theoretical aspects to build innovative methodologies, products and services to tackle real-world challenges facing our planet.
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5

Platzer, Andre. Stochastic Differential Dynamic Logic for Stochastic Hybrid Programs. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada543485.

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6

Aliprantis, Dionissi, Daniel R. Carroll, and Eric R. Young. The Dynamics of the Racial Wealth Gap. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-201918r.

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What drives the dynamics of the racial wealth gap? We answer this question using a dynamic stochastic general equilibrium heterogeneous-agents model. Our calibrated model endogenously produces a racial wealth gap matching that observed in recent decades along with key features of the current cross-sectional distribution of wealth, earnings, intergenerational transfers, and race. Our model predicts that equalizing earnings is by far the most important mechanism for permanently closing the racial wealth gap. One-time wealth transfers have only transitory effects unless they address the racial earnings gap, and return gaps only matter when earnings inequality is reduced.
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7

Perdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211028.

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We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” and “Quantum Information Technologies in the Earth Sciences” – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geosciences. Taking aim at complex multiscale planetary problems, the roles of our flagships are put into evidence in different contexts, ranging from I) Interdisciplinary analytics, model design and dynamic prediction of hydro-climatic and broader geophysical criticalities and extremes across multiple spatiotemporal scales; to II) Sensing the pulse of our planet and detecting early warning signs of geophysical phenomena from Space with our Meteoceanics QITES Constellation, at the interface between our latest developments in non-linear dynamics and emerging quantum technologies.
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8

Weerasinghe, Ananda P. Controlled Stochastic Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada470046.

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9

Perdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211021.

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Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.
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10

Venkatachalapathy, Rajesh. Systems Isomorphisms in Stochastic Dynamic Systems. Portland State University Library, December 2019. http://dx.doi.org/10.15760/etd.7283.

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