Books: 'Network Dynamics Simulation'

1

Systems biology: Simulation of dynamic network states. Cambridge, UK: Cambridge University Press, 2011.

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2

Gilbert, Nigel, Petra Ahrweiler, and Andreas Pyka, eds. Simulating Knowledge Dynamics in Innovation Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43508-3.

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3

Lamers, Eugen. Contributions to Simulation Speed-Up: Rare Event Simulation and Short-Term Dynamic Simulation for Mobile Network Planning. Wiesbaden: Vieweg+Teubner / GWV Fachverlage GmbH, Wiesbaden, 2008.

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4

Nuzzolo, Agostino. Transit network modelling: The schedule- based dynamic approach. Milano: F. Angeli, 2003.

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5

A, Abrahamsen Adele, ed. Connectionism and the mind: Parallel processing, dynamics, and evolution in networks. 2nd ed. Malden, MA: Blackwell, 2002.

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6

Desideri, Umberto, Giampaolo Manfrida, and Enrico Sciubba, eds. ECOS 2012. Florence: Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-322-9.

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The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology.

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7

Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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8

United States. National Aeronautics and Space Administration., ed. The coupling of fluids, dynamics, and controls on Advanced Architecture Computers. [Washington, DC: National Aeronautics and Space Administration, 1995.

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9

Magnus, Nørgaard, ed. Neural networks for modelling and control of dynamic systems: A practitioner's handbook. Berlin: Springer, 2000.

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10

Astrophysics School (6th 1993 Thessalonikē, Greece). Galactic dynamics and n-body simulations: Lectures held at the Astrophysics School VI, organized by the European Astrophysics Doctoral Network (EADN) in Thessaloniki, Greece, 13-23 July 1993. Berlin: Springer-Verlag, 1994.

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11

Zundong, Zhang, and Qin Yong active 2013, eds. Cheng shi dao lu jiao tong wang luo duo mo tai jie gou dong tai xing yan jiu: Multimodal Dynamics of Urban Road Traffic Networks. Beijing: Ke xue chu ban she, 2013.

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12

Vasca, Francesco. Dynamics and Control of Switched Electronic Systems: Advanced Perspectives for Modeling, Simulation and Control of Power Converters. London: Springer London, 2012.

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13

Heyde, Chris Oliver. Dynamic voltage security assessment for on-line control room application =: (Dynamische Spannungsstabilitätsrechnungen als online Entscheidungsgrundlage für die Leitwarte). Magdeburg: Otto-von-Guericke-Universität Magdeburg, 2010.

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14

Meade, Douglas S., ed. In Quest of the Craft. Florence: Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-820-0.

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INFORUM is a research project started more than forty five years ago by Clopper Almon. The focus is on the development of dynamic, interindustry, macroeconometric models to forecast the economy in the long run. Over the last 30 years, the Inforum approach to model building has been shared by economists in many different countries. Researchers have focused much of their efforts to developing a linked system of international interindustry models with a consistent methodology. A world-wide network of research associates use similar methods and a common software obtaining comparable results to produce studies of common interest to the group. Inforum partners have shared their research in an annual conference since 1993. The XXII Inforum World Conference was held in Alexandria, Virginia in September 2014 and this book contains a selection of papers presented during the sessions. All these contributions share an empirical and pragmatic orientation that is very useful for policymakers, business, and applied economists. Some papers are devoted to specific topics (productivity, energy, international trade, demographic changes) and some others are oriented to model building and simulations.

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15

Peters, Martin. Computational Fluid Dynamics for Sport Simulation. Springer, 2016.

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16

Computational Fluid Dynamics for Sport Simulation. Springer, 2009.

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17

Peters, Martin. Computational Fluid Dynamics for Sport Simulation. Springer, 2010.

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18

Computational Fluid Dynamics For Sport Simulation. Springer, 2009.

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19

Modeling Control Channel Dynamics of the SAAM Architecture Using the NS Network Simulation Tool. Storming Media, 1999.

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20

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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21

Lenhard, Johannes. Computation and Simulation. Edited by Robert Frodeman. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198733522.013.36.

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Simulation modeling makes use of computational resources in fields that are not of a mathematical nature. The first part of this chapter introduces four phases of the historical development of computation and simulation (C&S) and discusses their relationship to interdisciplinarity. Starting with a pioneering phase, C&S went through disciplinary specialization, ubiquitous diffusion, and a recent infrastructural turn. The second part of the chapter explores aspects of the interdisciplinary dynamics of C&S, especially the interplay between complexity, experimentation, and visualization. Then, C&S is put in relationship to Shinn’s “research technology” and Galison’s “trading zones.” Finally, the chapter observes a recent turn toward network-like interdisciplinary integration.

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22

Monge, Peter R., and Noshir Contractor. Theories of Communication Networks. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195160369.001.0001.

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To date, most network research contains one or more of five major problems. First, it tends to be atheoretical, ignoring the various social theories that contain network implications. Second, it explores single levels of analysis rather than the multiple levels out of which most networks are comprised. Third, network analysis has employed very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finally, almost all the research is static and cross-sectional rather than dynamic. Theories of Communication Networks presents solutions to all five problems. The authors develop a multitheoretical model that relates different social science theories with different network properties. This model is multilevel, providing a network decomposition that applies the various social theories to all network levels: individuals, dyads, triples, groups, and the entire network. The book then establishes a model from the perspective of complex adaptive systems and demonstrates how to use Blanche, an agent-based network computer simulation environment, to generate and test network theories and hypotheses. It presents recent developments in network statistical analysis, the p* family, which provides a basis for valid multilevel statistical inferences regarding networks. Finally, it shows how to relate communication networks to other networks, thus providing the basis in conjunction with computer simulations to study the emergence of dynamic organizational networks.

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23

Palsson, Bernhard Ø. Systems Biology: Simulation of Dynamic Network States. Cambridge University Press, 2011.

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24

Palsson, Bernhard Ø. Systems Biology: Simulation of Dynamic Network States. Cambridge University Press, 2012.

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25

Palsson, Bernhard Ø. Systems Biology: Simulation of Dynamic Network States. Cambridge University Press, 2011.

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26

Palsson, Bernhard Ø. Systems Biology: Simulation of Dynamic Network States. Cambridge University Press, 2011.

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27

Gilbert, Nigel, Andreas Pyka, and Petra Ahrweiler. Simulating Knowledge Dynamics in Innovation Networks. Springer, 2014.

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28

Gilbert, Nigel, Andreas Pyka, and Petra Ahrweiler. Simulating Knowledge Dynamics in Innovation Networks. Springer, 2016.

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29

Gilbert, Nigel, Andreas Pyka, and Petra Ahrweiler. Simulating Knowledge Dynamics in Innovation Networks. Gilbert Nigel, 2014.

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30

Snijders, Tom A. B., and Mark Pickup. Stochastic Actor Oriented Models for Network Dynamics. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.10.

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Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are snapshots of the resulting changing network. The statistical analysis is based on computer simulations of this process, which provides a great deal of flexibility in representing data constraints and dependence structures. In this Chapter we begin by defining the basic model. We then explicate a new model for nondirected ties, including several options for the specification of how pairs of actors coordinate tie changes. Next, we describe coevolution models. These can be used to model the dynamics of several interdependent sets of variables, such as the analysis of panel data on a network and the behavior of the actors in the network, or panel data on two or more networks. We finish by discussing the differences between Stochastic Actor Oriented Models and some other longitudinal network models. A major distinguishing feature is the treatment of time, which allows straightforward application of the model to panel data with different time lags between waves. We provide a variety of applications in political science throughout.

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31

Li, Changpin. Recent Advances in Applied Nonlinear Dynamics with Numerical Analysis: Fractional Dynamics, Network Dynamics, Classical Dynamics and Fractal Dynamics with Their Numerical Simulations. World Scientific Publishing Co Pte Ltd, 2013.

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32

Musa, MD. Modeling chlorine concentration in pipe networks. 1991.

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33

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

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34

Bernasconi, Marco, Carlo Massobrio, Jincheng Du, and Philip S. Salmon. Molecular Dynamics Simulations of Disordered Materials: From Network Glasses to Phase-Change Memory Alloys. Springer, 2016.

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35

Bernasconi, Marco, Carlo Massobrio, Jincheng Du, and Philip S. Salmon. Molecular Dynamics Simulations of Disordered Materials: From Network Glasses to Phase-Change Memory Alloys. Springer, 2015.

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36

Bernasconi, Marco, Carlo Massobrio, Jincheng Du, and Philip S. Salmon. Molecular Dynamics Simulations of Disordered Materials: From Network Glasses to Phase-Change Memory Alloys. Springer, 2015.

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37

Ding, Dong, Yan Wang, Zhicheng Ji, Ju H. Park, and Ze Tang. Impulsive Synchronization of Complex Dynamical Networks: Modeling, Control and Simulations. Springer Singapore Pte. Limited, 2021.

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38

Ding, Dong, Yan Wang, Zhicheng Ji, Ju H. Park, and Ze Tang. Impulsive Synchronization of Complex Dynamical Networks: Modeling, Control and Simulations. Springer, 2022.

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39

Abrahamsen, Adele, and William Bechtel. Connectionism and the Mind: Parallel Processing, Dynamics, and Evolution in Networks. Blackwell Publishing Limited, 2001.

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40

Zaheer Ul-Haq and Angela K. Wilson, eds. Frontiers in Computational Chemistry: Volume 6. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150368481220601.

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Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. The sixth volume of this series features these six different perspectives on the application of computational chemistry in rational drug design: 1. Computer-aided molecular design in computational chemistry 2. The role of ensemble conformational sampling using molecular docking & dynamics in drug discovery 3. Molecular dynamics applied to discover antiviral agents 4. Pharmacophore modeling approach in drug discovery against the tropical infectious disease malaria 5. Advances in computational network pharmacology for Traditional Chinese Medicine (TCM) research 6. Progress in electronic-structure based computational methods: from small molecules to large molecular systems of biological significance

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41

Islam, M. Rashidul. Modeling of chlorine concentration in unsteady flows in pipe networks. 1995.

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42

Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

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43

Abrahamsen, Adele, and William Bechtel. Connectionism and the Mind : Parallel Processing Dynamics and Evolution. Blackwell Publishing Limited, 2001.

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44

Contopoulos, Georgios Ioannou, and N. Spyrou. Galactic Dynamics and N-Body Simulations: Lectures Held at the Astrophysics School Vi, Organized by the European Astrophysics Doctoral Network (Lecture Notes in Physics). Springer, 1994.

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45

The coupling of fluids, dynamics, and controls on Advanced Architecture Computers. [Washington, DC: National Aeronautics and Space Administration, 1995.

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46

Iannelli, Luigi, and Francesco Vasca. Dynamics and Control of Switched Electronic Systems: Advanced Perspectives for Modeling, Simulation and Control of Power Converters. Springer, 2014.

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47

Iannelli, Luigi, and Francesco Vasca. Dynamics and Control of Switched Electronic Systems: Advanced Perspectives for Modeling, Simulation and Control of Power Converters. Springer, 2012.

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48

Burguillo, Juan C. Self-organizing Coalitions for Managing Complexity: Agent-based Simulation of Evolutionary Game Theory Models using Dynamic Social Networks for ... Springer, 2017.

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49

Burguillo, Juan C. Self-organizing Coalitions for Managing Complexity: Agent-based Simulation of Evolutionary Game Theory Models using Dynamic Social Networks for ... Springer, 2018.

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50

Norgaard, M., O. Ravn, N. K. Poulsen, and L. K. Hansen. Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook (Advanced Textbooks in Control and Signal Processing). Springer, 2003.

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Books on the topic 'Network Dynamics Simulation'

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