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Bibliographies thématiques / Dynamical system modeling / Livres

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Livres sur le sujet « Dynamical system modeling »

Auteur : Grafiati

Publié le 29 mars 2025

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1

Fuchs, Hans U. Modeling of uniform dynamical systems : A system dynamics approach. Zürich : Füssli, 2002.

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2

Goebel, Rafal. Hybrid dynamical systems : Modeling, stability, and robustness. Princeton, N.J : Princeton University Press, 2012.

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3

Palm, William J. Modeling, analysis, and control of dynamic systems. 2^e éd. New York : Wiley, 1998.

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4

Palm, William J. Modeling, analysis, and control of dynamic systems. 2^e éd. New York : Wiley, 1999.

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5

Mukherjee, Animesh. Dynamics On and Of Complex Networks, Volume 2 : Applications to Time-Varying Dynamical Systems. New York, NY : Springer New York, 2013.

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6

Abarbanel, Henry. Predicting the Future : Completing Models of Observed Complex Systems. New York, NY : Springer New York, 2013.

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7

Beltrami, Edward J. Mathematics for dynamic modeling. Boston : Academic Press, 1987.

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8

Beltrami, Edward J. Mathematics for dynamic modeling. 2^e éd. Boston : Academic Press, 1998.

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9

Awrejcewicz, Jan, dir. Dynamical Systems : Modelling. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42402-6.

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10

L, Margolis Donald, et Rosenberg Ronald C, dir. System dynamics : Modeling and simulation of mechatronic systems. 3^e éd. New York : Wiley, 2000.

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11

Karnopp, Dean. System dynamics : Modeling and simulation of mechatronic systems. 5^e éd. Hoboken, NJ : Wiley, 2012.

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12

L, Margolis Donald, et Rosenberg Ronald C, dir. System dynamics : Modeling and simulation of mechatronic systems. 4^e éd. Hoboken, N.J : John Wiley & Sons, 2005.

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13

Coyle, R. G. System Dynamics Modelling. Boston, MA : Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2935-8.

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14

Clark, Rolf. System dynamics and modeling. Arlington, Va : Operations Research Society of America, 1988.

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15

Vu, Hung V. Dynamic systems : Modeling and analysis. London : McGraw-Hill, 1998.

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16

Takács, Gergely. Model Predictive Vibration Control : Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. London : Springer London, 2012.

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17

Fabien, Brian C. Analytical system dynamics : Modeling and simulation. New York : Springer Science+Business Media, 2009.

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18

Duggan, Jim. System Dynamics Modeling with R. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34043-2.

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19

Layer, Edward. Modelling of Simplified Dynamical Systems. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56098-9.

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Layer, Edward. Modelling of Simplified Dynamical Systems. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002.

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21

Hannon, Bruce, et Matthias Ruth. Modeling Dynamic Biological Systems. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05615-9.

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22

Ruth, Matthias, et Bruce Hannon. Modeling Dynamic Biological Systems. New York, NY : Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0651-4.

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23

Ruth, Matthias, et Bruce Hannon. Modeling Dynamic Economic Systems. New York, NY : Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2268-2.

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24

Robinson, Walter A. Modeling Dynamic Climate Systems. New York, NY : Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0113-4.

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Ruth, Matthias, et Bruce Hannon. Modeling Dynamic Economic Systems. Boston, MA : Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2209-9.

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26

Matthias, Ruth, dir. Modeling dynamic biological systems. New York : Springer, 1997.

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27

Ruth, Matthias. Modeling Dynamic Economic Systems. 2^e éd. Boston, MA : Springer US, 2012.

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28

Ruth, Matthias. Modeling Dynamic Economic Systems. New York, NY : Springer New York, 1997.

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29

M, Hannon Bruce, dir. Modeling dynamic economic systems. New York : Springer, 1997.

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30

Torkel, Glad, dir. Modeling of dynamic systems. Englewood Cliffs, N.J : PTR Prentice Hall, 1994.

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31

Shearer, J. Lowen. Dynamic modeling and control of engineering systems. New York : Macmillan, 1990.

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32

P. P. J. van den Bosch et A. C. van der Klauw. Modeling, Identification and Simulation of Dynamical Systems. Taylor & Francis Group, 2020.

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33

P. P. J. van den Bosch et A. C. van der Klauw. Modeling, Identification and Simulation of Dynamical Systems. Taylor & Francis Group, 2020.

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34

P. P. J. van den Bosch et A. C. van der Klauw. Modeling, Identification and Simulation of Dynamical Systems. Taylor & Francis Group, 2020.

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35

Goebel, Rafal, Andrew R. Teel et Ricardo G. Sanfelice. Hybrid Dynamical Systems : Modeling, Stability, and Robustness. Princeton University Press, 2012.

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36

Goebel, Rafal, Andrew R. Teel et Ricardo G. Sanfelice. Hybrid Dynamical Systems : Modeling, Stability, and Robustness. Princeton University Press, 2012.

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37

Modeling complex systems. 2^e éd. New York : Springer, 2010.

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38

Mathematics of complexity and dynamical systems. New York : Springer, 2012.

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39

Ganguly, Niloy, Animesh Mukherjee, Monojit Choudhury, Fernando Peruani et Bivas Mitra. Dynamics On and Of Complex Networks, Volume 2 : Applications to Time-Varying Dynamical Systems. Birkhäuser, 2015.

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40

Dynamics On and Of Complex Networks Volume 2 Modeling and Simulation in Science Engineering and Technology. Springer-Verlag New York Inc., 2013.

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41

Abarbanel, Henry. Predicting the Future : Completing Models of Observed Complex Systems. Springer New York, 2016.

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42

Gao, Yanhong, et Deliang Chen. Modeling of Regional Climate over the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.591.

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The modeling of climate over the Tibetan Plateau (TP) started with the introduction of Global Climate Models (GCMs) in the 1950s. Since then, GCMs have been developed to simulate atmospheric dynamics and eventually the climate system. As the highest and widest international plateau, the strong orographic forcing caused by the TP and its impact on general circulation rather than regional climate was initially the focus. Later, with growing awareness of the incapability of GCMs to depict regional or local-scale atmospheric processes over the heterogeneous ground, coupled with the importance of this information for local decision-making, regional climate models (RCMs) were established in the 1970s. Dynamic and thermodynamic influences of the TP on the East and South Asia summer monsoon have since been widely investigated by model. Besides the heterogeneity in topography, impacts of land cover heterogeneity and change on regional climate were widely modeled through sensitivity experiments.In recent decades, the TP has experienced a greater warming than the global average and those for similar latitudes. GCMs project a global pattern where the wet gets wetter and the dry gets drier. The climate regime over the TP covers the extreme arid regions from the northwest to the semi-humid region in the southeast. The increased warming over the TP compared to the global average raises a number of questions. What are the regional dryness/wetness changes over the TP? What is the mechanism of the responses of regional changes to global warming? To answer these questions, several dynamical downscaling models (DDMs) using RCMs focusing on the TP have recently been conducted and high-resolution data sets generated. All DDM studies demonstrated that this process-based approach, despite its limitations, can improve understandings of the processes that lead to precipitation on the TP. Observation and global land data assimilation systems both present more wetting in the northwestern arid/semi-arid regions than the southeastern humid/semi-humid regions. The DDM was found to better capture the observed elevation dependent warming over the TP. In addition, the long-term high-resolution climate simulation was found to better capture the spatial pattern of precipitation and P-E (precipitation minus evapotranspiration) changes than the best available global reanalysis. This facilitates new and substantial findings regarding the role of dynamical, thermodynamics, and transient eddies in P-E changes reflected in observed changes in major river basins fed by runoff from the TP. The DDM was found to add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation.Although these advantages in the DDM over the TP are evidenced, there are unavoidable facts to be aware of. Firstly, there are still many discrepancies that exist in the up-to-date models. Any uncertainty in the model’s physics or in the land information from remote sensing and the forcing could result in uncertainties in simulation results. Secondly, the question remains of what is the appropriate resolution for resolving the TP’s heterogeneity. Thirdly, it is a challenge to include human activities in the climate models, although this is deemed necessary for future earth science. All-embracing further efforts are expected to improve regional climate models over the TP.

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43

(Foreword), D. H. Meadows, dir. Dynamic Modeling (Modeling Dynamic Systems). Springer, 2001.

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44

Giunti, Marco. Computation, Dynamics, and Cognition. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195090093.001.0001.

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Currently there is growing interest in the application of dynamical methods to the study of cognition. Computation, Dynamics, and Cognition investigates this convergence from a theoretical and philosophical perspective, generating a provocative new view of the aims and methods of cognitive science. Advancing the dynamical approach as the methodological frame best equipped to guide inquiry in the field's two main research programs--the symbolic and connectionist approaches--Marco Giunti engages a host of questions crucial not only to the science of cognition, but also to computation theory, dynamical systems theory, philosophy of mind, and philosophy of science. In chapter one Giunti employs a dynamical viewpoint to explore foundational issues in computation theory. Using the concept of Turing computability, he precisely and originally defines the nature of a computational system, sharpening our understanding of computation theory and its applications. In chapter two he generalizes his definition of a computational system, arguing that the concept of Turing computability itself is relative to the kind of support on which Turing machine operate. Chapter three completes the book's conceptual foundation, discussing a form of scientific explanation for real dynamical systems that Giunti calls "Galilean explanation." The book's fourth and final chapter develops the methodological thesis that all cognitive systems are dynamical systems. On Giunti's view, a dynamical approach is likely to benefit even those scientific explanations of cognition which are based on symbolic models. Giunti concludes by proposing a new modeling practice for cognitive science, one based on "Galilean models" of cognitive systems. Innovative, lucidly-written, and broad-ranging in its analysis, Computation, Dynamics, and Cognition will interest philosophers of science and mind, as well as cognitive scientists, computer scientists, and theorists of dynamical systems. This book elaborates a comprehensive picture of the application of dynamical methods to the study of cognition. Giunti argues that both computational systems and connectionist networks are special types of dynamical systems. He shows how this dynamical approach can be applied to problems of cognition, information processing, consciousness, meaning, and the relation between body and mind.

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45

Rosenberg, Ronald C., Dean C. Karnopp et Donald L. Margolis. System Dynamics : Modeling and Simulation of Mechatronic Systems. Wiley, 2006.

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46

Rosenberg, Ronald C., Dean C. Karnopp et Donald L. Margolis. System Dynamics : Modeling and Simulation of Mechatronic Systems. 3^e éd. Wiley-Interscience, 1999.

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47

Rosenberg, Ronald C., Dean C. Karnopp et Donald L. Margolis. System Dynamics : Modeling and Simulation of Mechatronic Systems. Wiley & Sons, Incorporated, John, 2007.

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48

System Dynamics Modelling. 1996.

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49

Brauer, Fred, et Christopher Kribs. Dynamical Systems for Biological Modeling. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b20687.

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50

Takács, Gergely, et Boris Rohaľ-Ilkiv. Model Predictive Vibration Control : Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. Springer, 2014.

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