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Journal articles on the topic 'Continuous time simulation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Patrick, Steven. "Linking Empirical Data to Continuous-Time, Continuous-State Computer Simulation." Social Science Computer Review 11, no. 1 (1993): 33–47. http://dx.doi.org/10.1177/089443939301100104.

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2

Kendon, Viv. "Quantum computing using continuous-time evolution." Interface Focus 10, no. 6 (2020): 20190143. http://dx.doi.org/10.1098/rsfs.2019.0143.

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Computational methods are the most effective tools we have besides scientific experiments to explore the properties of complex biological systems. Progress is slowing because digital silicon computers have reached their limits in terms of speed. Other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. Quantum computing exploits the coherence and superposition properties of quantum systems to explore many possible computational paths in parallel. This provides a fundamentally more efficient route

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3

Mattsson, Sven Erik. "Simulation of object-oriented continuous time models." Mathematics and Computers in Simulation 39, no. 5-6 (1995): 513–18. http://dx.doi.org/10.1016/0378-4754(94)00112-6.

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4

Benabes, P. "Accurate Time-Domain Simulation of Continuous-Time Sigma–Delta Modulators." IEEE Transactions on Circuits and Systems I: Regular Papers 56, no. 10 (2009): 2248–58. http://dx.doi.org/10.1109/tcsi.2008.2012224.

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5

Biller, Peter, and Francesco Petruccione. "Continuous time simulation of transient polymer network models." Journal of Chemical Physics 92, no. 10 (1990): 6322–26. http://dx.doi.org/10.1063/1.458309.

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6

Petruccione, Francesco, and Peter Biller. "Continuous time simulation of the Doi–Edwards model." Journal of Chemical Physics 92, no. 10 (1990): 6327–31. http://dx.doi.org/10.1063/1.458310.

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7

Padoan, Pier Carlo. "Nonlinear simulation analysis in continuous time econometric models." Computers & Mathematics with Applications 24, no. 8-9 (1992): 57–65. http://dx.doi.org/10.1016/0898-1221(92)90187-m.

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8

Husain, I., and Z. Jabeen. "Continuous-time fractional minmax programming." Mathematical and Computer Modelling 42, no. 5-6 (2005): 701–10. http://dx.doi.org/10.1016/j.mcm.2003.10.055.

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9

Wolf, Douglas A. "Simulation Methods for Analyzing Continuous-Time Event-History Models." Sociological Methodology 16 (1986): 283. http://dx.doi.org/10.2307/270926.

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10

Raczynski, Stanislaw. "Continuous Simulation, Differential Inclusions, Uncertainty, and Traveling in Time." SIMULATION 80, no. 2 (2004): 87–100. http://dx.doi.org/10.1177/0037549704042858.

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11

Schell, Bob, and Yannis Tsividis. "Analysis and simulation of continuous-time digital signal processors." Signal Processing 89, no. 10 (2009): 2013–26. http://dx.doi.org/10.1016/j.sigpro.2009.04.005.

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12

Elsheikh, Atiyah. "Derivative-based hybrid heuristics for continuous-time simulation optimization." Simulation Modelling Practice and Theory 46 (August 2014): 164–75. http://dx.doi.org/10.1016/j.simpat.2013.11.011.

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13

Yamamoto, Yuji, Hiroyuki Torii, Yoshitaka Maekawa, Mitsuo Tamura, Hironori Kasahara, and Seinosuke Narita. "Parallel Processing of Continuous/Discrete-Time Control Systems Simulation." IEEJ Transactions on Electronics, Information and Systems 113, no. 11 (1993): 939–46. http://dx.doi.org/10.1541/ieejeiss1987.113.11_939.

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14

Gupta, Sandeep. "Polynomial interpolation between input samples for continuous-time simulation." Journal of Guidance, Control, and Dynamics 17, no. 6 (1994): 1369–71. http://dx.doi.org/10.2514/3.21359.

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15

Biller, Peter, and Francesco Petruccione. "Continuous time simulation of transient polymer networks: Rheological properties." Makromolekulare Chemie. Macromolecular Symposia 45, no. 1 (1991): 169–75. http://dx.doi.org/10.1002/masy.19910450120.

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16

Glasserman, Paul. "Derivative Estimates from Simulation of Continuous-Time Markov Chains." Operations Research 40, no. 2 (1992): 292–308. http://dx.doi.org/10.1287/opre.40.2.292.

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17

Comte, F. "SIMULATION AND ESTIMATION OF LONG MEMORY CONTINUOUS TIME MODELS." Journal of Time Series Analysis 17, no. 1 (1996): 19–36. http://dx.doi.org/10.1111/j.1467-9892.1996.tb00262.x.

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18

Ward, Thomas L., Patricia A. S. Ralston, and Denise J. C. Stottmann. "Continuous-time simulation of metal cutting on a lathe." Computers & Industrial Engineering 20, no. 3 (1991): 313–22. http://dx.doi.org/10.1016/0360-8352(91)90003-o.

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19

Louhenkilpi, Seppo, Erkki Laitinen, and Risto Nieminen. "Real-time simulation of heat transfer in continuous casting." Metallurgical Transactions B 24, no. 4 (1993): 685–93. http://dx.doi.org/10.1007/bf02673184.

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20

Angeli, Letizia, Stefan Grosskinsky, Adam M. Johansen, and Andrea Pizzoferrato. "Rare Event Simulation for Stochastic Dynamics in Continuous Time." Journal of Statistical Physics 176, no. 5 (2019): 1185–210. http://dx.doi.org/10.1007/s10955-019-02340-1.

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21

Ben-Talha, Hiba, Paolo Massioni, and Gérard Scorletti. "Robust simulation of continuous-time systems with rational dynamics." International Journal of Robust and Nonlinear Control 27, no. 16 (2016): 3097–108. http://dx.doi.org/10.1002/rnc.3728.

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22

Song, Xiao, Yaofei Ma, Wei Zhang, and Jiangyun Wang. "Quantized State Based Simulation of Time Invariant and Time Varying Continuous Systems." Mathematical Problems in Engineering 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/141607.

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Continuous system can be discretized for computer simulation. Quantized state systems (QSS) method has been used to discretize time invariant systems based on the discretization of the state space. A HLA based QSS method is proposed in this paper to address issues of real-time advancements in simulation and an aircraft control example was introduced to illustrate our method. Moreover, to simulate time varying systems, a novel approach is also proposed and exemplified with a practical case.

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23

Li, Quan-Lin, and Chuang Lin. "Continuous-Time QBD Processes with Continuous Phase Variable." Computers & Mathematics with Applications 52, no. 10-11 (2006): 1483–510. http://dx.doi.org/10.1016/j.camwa.2006.07.003.

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24

Al-Assadi, Salem A. K., and Azzam A. Marouf. "Optimal discrete-time models for continuous-time control systems." Applied Mathematical Modelling 12, no. 5 (1988): 533–42. http://dx.doi.org/10.1016/0307-904x(88)90090-x.

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25

Yin, X., and H. Schwarz. "A Hybrid Method for Real-Time Simulation of Continuous-Time Bilinear Systems." IFAC Proceedings Volumes 25, no. 20 (1992): 289–94. http://dx.doi.org/10.1016/s1474-6670(17)49877-6.

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26

AGLIARI, ELENA, OLIVER MÜLKEN, and ALEXANDER BLUMEN. "CONTINUOUS-TIME QUANTUM WALKS AND TRAPPING." International Journal of Bifurcation and Chaos 20, no. 02 (2010): 271–79. http://dx.doi.org/10.1142/s0218127410025715.

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Recent findings suggest that processes such as the excitonic energy transfer through the photosynthetic antenna display quantal features, aspects known from the dynamics of charge carriers along polymer backbones. Hence, in modeling energy transfer one has to leave the classical, master-equation-type formalism and advance towards an increasingly quantum-mechanical picture, while still retaining a local description of the complex network of molecules involved in the transport, say through a tight-binding approach. Interestingly, the continuous time random walk (CTRW) picture, widely employed in

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27

YU, SIMIN, and GUANRONG CHEN. "CHAOTIFYING CONTINUOUS-TIME NONLINEAR AUTONOMOUS SYSTEMS." International Journal of Bifurcation and Chaos 22, no. 09 (2012): 1250232. http://dx.doi.org/10.1142/s021812741250232x.

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Based on the principle of chaotification for continuous-time autonomous systems, which relies on two basic properties of chaos, i.e. being globally bounded with necessary positive-zero-negative Lyapunov exponents, this paper derives a feasible and unified chaotification method for designing a general chaotic continuous-time autonomous nonlinear system. For a system consisting of a linear and a nonlinear subsystems, chaotification is achieved using separation of state variables, which decomposes the system into two open-loop subsystems interacting through mutual feedback resulting in an overall

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28

Pereira, Ana Paula De Paiva, João Paulo Fernandes, Allbens Picardi Faria Atman, and José Luiz Acebal. "Simulation and Calibration Between Parameters of Continuous Time Random Walks and Subdifusive Model." TEMA (São Carlos) 18, no. 2 (2017): 0305. http://dx.doi.org/10.5540/tema.2017.018.02.0305.

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We address the problem of subdiusion or normal diusion to perform a calibration between the parameters used in simulation and the parameters of a subdifusive model. The theoretical model is written as a generalized diusion equation with fractional derivatives in time. The data is generated by simulations consisting of continuous-time random walks with controlled mean waiting time and jump length variance to provide a full range of cases between subdiusion andnormal diusion. From the simulations, we compare the accuracy of two methods to obtain the diusion constant, the order of fractional deri

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29

Luhmer, Alfred, Alois Steindl, Gustav Feichtinger, Richard Hartl, and Gerhard Sorger. "Adpuls in continuous time." European Journal of Operational Research 34, no. 2 (1988): 171–77. http://dx.doi.org/10.1016/0377-2217(88)90352-9.

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30

Liu, Chang, and Duane A. McVay. "Continuous Reservoir-Simulation-Model Updating and Forecasting Improves Uncertainty Quantification." SPE Reservoir Evaluation & Engineering 13, no. 04 (2010): 626–37. http://dx.doi.org/10.2118/119197-pa.

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Summary Most reservoir-simulation studies are conducted in a static context—at a single point in time using a fixed set of historical data for history matching. Time and budget constraints usually result in significant reduction in the number of uncertain parameters and incomplete exploration of the parameter space, which results in underestimation of forecast uncertainty and less-than-optimal decision making. Markov Chain Monte Carlo (MCMC) methods have been used in static studies for rigorous exploration of the parameter space for quantification of forecast uncertainty, but these methods suf

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31

Eliazar, Iddo. "EXTREMES: A CONTINUOUS-TIME PERSPECTIVE." Probability in the Engineering and Informational Sciences 19, no. 3 (2005): 289–308. http://dx.doi.org/10.1017/s0269964805050163.

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We consider a generic continuous-time system in which events of random magnitudes occur stochastically and study the system's extreme-value statistics. An event is described by a pair (t,x) of coordinates, wheretis the time at which the event took place andxis the magnitude of the event. The stochastic occurrence of the events is assumed to be governed by a Poisson point process.We study various issues regarding the system's extreme-value statistics, including (i) the distribution of the largest-magnitude event, the distribution of thenth “runner-up” event, and the multidimensional distributio

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32

van Dijk, N. M., and P. G. Taylor. "An error bound for a continuous time approximation of discrete time servicing." Communications in Statistics. Stochastic Models 8, no. 4 (1992): 651–64. http://dx.doi.org/10.1080/15326349208807245.

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33

Stadje, Wolfgang. "First exit times for integer valued continuous time markov chains." Sequential Analysis 19, no. 3 (2000): 93–114. http://dx.doi.org/10.1080/07474940008836443.

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34

Soares, Marta O., and Luísa Canto e Castro. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis." PharmacoEconomics 30, no. 12 (2012): 1101–17. http://dx.doi.org/10.2165/11599380-000000000-00000.

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35

Alves Santos, Raúl, Julio E. Normey-Rico, Alejandro Merino Gómez, Luis Felipe Acebes Arconada, and César de Prada Moraga. "OPC based distributed real time simulation of complex continuous processes." Simulation Modelling Practice and Theory 13, no. 7 (2005): 525–49. http://dx.doi.org/10.1016/j.simpat.2005.01.005.

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36

Pavan, S. "Efficient Simulation of Weak Nonlinearities in Continuous-Time Oversampling Converters." IEEE Transactions on Circuits and Systems I: Regular Papers 57, no. 8 (2010): 1925–34. http://dx.doi.org/10.1109/tcsi.2009.2039833.

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37

Lee, S., X. Zhao, A. Shendarkar, K. Vasudevan, and Y. Son. "EPOCH TIME SYNCHRONIZATION METHOD WITH CONTINUOUS UPDATE FOR DISTRIBUTED SIMULATION." IFAC Proceedings Volumes 39, no. 3 (2006): 603–8. http://dx.doi.org/10.3182/20060517-3-fr-2903.00308.

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38

Nicol, D. M., and P. Heidelberger. "Optimistic Parallel Simulation of Continuous Time Markov Chains Using Uniformization." Journal of Parallel and Distributed Computing 18, no. 4 (1993): 395–410. http://dx.doi.org/10.1006/jpdc.1993.1073.

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39

Ashry, A., and H. Aboushady. "Fast and accurate jitter simulation technique for continuous-time modulators." Electronics Letters 45, no. 24 (2009): 1218. http://dx.doi.org/10.1049/el.2009.2707.

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40

Oya, Antonia, Jesús Navarro-Moreno, and Juan Carlos Ruiz-Molina. "Widely Linear Simulation of Continuous-Time Complex-Valued Random Signals." IEEE Signal Processing Letters 18, no. 9 (2011): 513–16. http://dx.doi.org/10.1109/lsp.2011.2161286.

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41

Heidelberger, P., and D. M. Nicol. "Conservative parallel simulation of continuous time Markov chains using uniformization." IEEE Transactions on Parallel and Distributed Systems 4, no. 8 (1993): 906–21. http://dx.doi.org/10.1109/71.238625.

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42

Bursi, Fabio, Andrea Ferrara, Andrea Grassi, and Chiara Ronzoni. "Simulating Continuous Time Production Flows in Food Industry by Means of Discrete Event Simulation." International Journal of Food Engineering 11, no. 1 (2015): 139–50. http://dx.doi.org/10.1515/ijfe-2014-0002.

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Abstract The paper presents a new framework for carrying out simulations of continuous-time stochastic processes by exploiting a discrete event approach. The application scope of this work mainly refers to industrial production processes executed on a continuous flow of material (e.g. food and beverage industry) as well as production processes working on discrete units but characterized by a high speed flow (e.g. automated packaging lines). The proposed model, developed adopting the Discrete EVent system Specification (DEVS) formalism, defines a single generalized base unit able to represent,

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43

Kalyon, M. "Design of Continuous Time Controllers Having Almost Minimum Time Response." Journal of Dynamic Systems, Measurement, and Control 124, no. 2 (2002): 252–60. http://dx.doi.org/10.1115/1.1468862.

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A class of near time-optimal nonlinear controllers—the Continuous Proximate Time-Optimal (CPTO) controllers—which involves continuous, rather than discontinuous, nonlinear feedback control functions is introduced. The CPTO controllers give near time-optimal response for large state errors, and provide smooth, stable response with near linear control for small state errors. In this paper, the CPTO controllers for three second-order (two stable and one unstable) plants and one third-order plant are introduced. Complete stability proofs, using the CPTO control law, are given for all the systems c

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44

Falin, G. "An error bound for a continuous time approximation of a time-sharing queue." Communications in Statistics. Stochastic Models 11, no. 1 (1995): 163–69. http://dx.doi.org/10.1080/15326349508807336.

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45

Huang, Yunda, Yuanyuan Zhang, Zong Zhang, and Peter B. Gilbert. "Generating Survival Times Using Cox Proportional Hazards Models with Cyclic and Piecewise Time-Varying Covariates." Statistics in Biosciences 12, no. 3 (2020): 324–39. http://dx.doi.org/10.1007/s12561-020-09266-3.

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Abstract Time-to-event outcomes with cyclic time-varying covariates are frequently encountered in biomedical studies that involve multiple or repeated administrations of an intervention. In this paper, we propose approaches to generating event times for Cox proportional hazards models with both time-invariant covariates and a continuous cyclic and piecewise time-varying covariate. Values of the latter covariate change over time through cycles of interventions and its relationship with hazard differs before and after a threshold within each cycle. The simulations of data are based on inverting

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46

Nkhoma, Mathews, Jaime Calbeto, Narumon Sriratanaviriyakul, Thu Muang, Quyen Ha Tran, and Thanh Kim Cao. "Towards an understanding of real-time continuous feedback from simulation games." Interactive Technology and Smart Education 11, no. 1 (2014): 45–62. http://dx.doi.org/10.1108/itse-03-2013-0005.

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Purpose – Simulation games have long been used as a teaching tool in the classroom environment mainly due to the high level of participation and engagement that students are able to generate from these, making the learning process more enjoyable and capable to replicate real-life scenarios. Feedback given during the simulation helps to motivate students to find better solutions to the problems being presented in the games and thus enhance their hands-on knowledge on particular subjects. The purpose of this research is to provide empirical evidence of interrelations and impacts that exist betwe

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47

Armand Eyebe Fouda, J. S., Bertrand Bodo, Samrat L. Sabat, and J. Yves Effa. "A Modified 0-1 Test for Chaos Detection in Oversampled Time Series Observations." International Journal of Bifurcation and Chaos 24, no. 05 (2014): 1450063. http://dx.doi.org/10.1142/s0218127414500631.

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The use of binary 0-1 test for chaos detection is limited to detect chaos in oversampled time series observations. In this paper we propose a modified 0-1 test in which, binary 0-1 test is applied to the discrete map of local maxima and minima of the original observable in contrast to the direct observable. The proposed approach successfully detects chaos in oversampled time series data. This is verified by simulating different numerical simulations of Lorenz and Duffing systems. The simulation results show the efficiency and computational gain of the proposed test for chaos detection in the c

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48

Cvitanić, Jakša, Xuhu Wan, and Jianfeng Zhang. "Optimal contracts in continuous-time models." Journal of Applied Mathematics and Stochastic Analysis 2006 (July 12, 2006): 1–27. http://dx.doi.org/10.1155/jamsa/2006/95203.

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We present a unified approach to solving contracting problems with full information in models driven by Brownian motion. We apply the stochastic maximum principle to give necessary and sufficient conditions for contracts that implement the so-called first-best solution. The optimal contract is proportional to the difference between the underlying process controlled by the agent and a stochastic, state-contingent benchmark. Our methodology covers a number of frameworks considered in the existing literature. The main finance applications of this theory are optimal compensation of company executi

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49

Johnson, Jean T. "Continuous-time, constant causative Markov chains." Stochastic Processes and their Applications 26 (1987): 161–71. http://dx.doi.org/10.1016/0304-4149(87)90057-3.

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50

Boshuizen, Frans A., and José M. Gouweleeuw. "A continuous-time job search model: general renewal processes." Communications in Statistics. Stochastic Models 11, no. 2 (1995): 349–69. http://dx.doi.org/10.1080/15326349508807349.

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