Relevant bibliographies by topics / Computational methods for Complex Systems

Academic literature on the topic 'Computational methods for Complex Systems'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Computational methods for Complex Systems"

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Jansen, Thomas L. C. "Computational spectroscopy of complex systems." Journal of Chemical Physics 155, no. 17 (November 7, 2021): 170901. http://dx.doi.org/10.1063/5.0064092.

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Numerous linear and non-linear spectroscopic techniques have been developed to elucidate structural and functional information of complex systems ranging from natural systems, such as proteins and light-harvesting systems, to synthetic systems, such as solar cell materials and light-emitting diodes. The obtained experimental data can be challenging to interpret due to the complexity and potential overlapping spectral signatures. Therefore, computational spectroscopy plays a crucial role in the interpretation and understanding of spectral observables of complex systems. Computational modeling of various spectroscopic techniques has seen significant developments in the past decade, when it comes to the systems that can be addressed, the size and complexity of the sample types, the accuracy of the methods, and the spectroscopic techniques that can be addressed. In this Perspective, I will review the computational spectroscopy methods that have been developed and applied for infrared and visible spectroscopies in the condensed phase. I will discuss some of the questions that this has allowed answering. Finally, I will discuss current and future challenges and how these may be addressed.
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Arora, J. S., and P. B. Thanedar. "Computational methods for optimum design of large complex systems." Computational Mechanics 1, no. 3 (1986): 221–42. http://dx.doi.org/10.1007/bf00272625.

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Udwadia, Firdaus E., and Nami Mogharabin. "New Directions in Modeling and Computational Methods for Complex Mechanical Dynamical Systems." Processes 10, no. 8 (August 9, 2022): 1560. http://dx.doi.org/10.3390/pr10081560.

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This paper presents a new conceptualization of complex nonlinear mechanical systems and develops new and novel computational methods for determining their response to given applied forces and torques. The new conceptualization uses the idea of including particles of zero mass to describe the dynamics of such systems. This leads to simplifications in the development of their equations of motion and engenders a straightforward new computational approach to simulate their behavior. The purpose of the paper is to develop a new analytical and computational methodology to handle complex systems and to illustrate it through the study of an old unsolved problem in classical mechanics, that of a non-uniform rigid spherical shell rolling, without slipping, under gravity on an arbitrary dimpled bowl-shaped rigid surface. The new conceptualization provides the explicit equations of motion for the system, the analytical determination of the reaction forces supplied by the surface, and a straightforward computational approach to simulate the dynamics. Detailed analytical and numerical results are provided. The computations illustrate the complexity of the dynamical behavior of the system and its high sensitivity to the initial orientation of the shell and to the presence of any initial angular velocity normal to the surface.
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Villaverde, Alejandro F., Carlo Cosentino, Attila Gábor, and Gábor Szederkényi. "Computational Methods for Identification and Modelling of Complex Biological Systems." Complexity 2019 (April 7, 2019): 1–3. http://dx.doi.org/10.1155/2019/4951650.

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Gu, Kai Liu and Guiding. "Improved PMHSS Iteration Methods for Complex Symmetric Linear Systems." Journal of Computational Mathematics 37, no. 2 (June 2019): 278–96. http://dx.doi.org/10.4208/jcm.1702-m2017-0007.

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Sosnowski, Marcin, Jaroslaw Krzywanski, and Radomír Ščurek. "Artificial Intelligence and Computational Methods in the Modeling of Complex Systems." Entropy 23, no. 5 (May 10, 2021): 586. http://dx.doi.org/10.3390/e23050586.

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Tavassoly, Iman, Joseph Goldfarb, and Ravi Iyengar. "Systems biology primer: the basic methods and approaches." Essays in Biochemistry 62, no. 4 (October 4, 2018): 487–500. http://dx.doi.org/10.1042/ebc20180003.

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Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.
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Locurcio, Marco, Francesco Tajani, and Pierluigi Morano. "Computational Methods Applied to Data Analysis for Modeling Complex Real Estate Systems." Complexity 2020 (July 9, 2020): 1–3. http://dx.doi.org/10.1155/2020/8519060.

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Puzynin, I. V., T. L. Boyadzhiev, S. I. Vinitskii, E. V. Zemlyanaya, T. P. Puzynina, and O. Chuluunbaatar. "Methods of computational physics for investigation of models of complex physical systems." Physics of Particles and Nuclei 38, no. 1 (February 2007): 70–116. http://dx.doi.org/10.1134/s1063779607010030.

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Puzyrkov, Dmitry, Sergey Polyakov, Viktoriia Podryga, and Sergey Markizov. "Concept of a Cloud Service for Data Preparation and Computational Control on Custom HPC Systems in Application to Molecular Dynamics." EPJ Web of Conferences 173 (2018): 05014. http://dx.doi.org/10.1051/epjconf/201817305014.

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At the present stage of computer technology development it is possible to study the properties and processes in complex systems at molecular and even atomic levels, for example, by means of molecular dynamics methods. The most interesting are problems related with the study of complex processes under real physical conditions. Solving such problems requires the use of high performance computing systems of various types, for example, GRID systems and HPC clusters. Considering the time consuming computational tasks, the need arises of software for automatic and unified monitoring of such computations. A complex computational task can be performed over different HPC systems. It requires output data synchronization between the storage chosen by a scientist and the HPC system used for computations. The design of the computational domain is also quite a problem. It requires complex software tools and algorithms for proper atomistic data generation on HPC systems. The paper describes the prototype of a cloud service, intended for design of atomistic systems of large volume for further detailed molecular dynamic calculations and computational management for this calculations, and presents the part of its concept aimed at initial data generation on the HPC systems.
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Dissertations / Theses on the topic "Computational methods for Complex Systems"

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Gravino, Pietro <1984&gt. "Novel investigation methods in Computational Social Dynamics and Complex Systems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6765/.

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In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.
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Ren, Xuchun. "Novel computational methods for stochastic design optimization of high-dimensional complex systems." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1738.

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The primary objective of this study is to develop new computational methods for robust design optimization (RDO) and reliability-based design optimization (RBDO) of high-dimensional, complex engineering systems. Four major research directions, all anchored in polynomial dimensional decomposition (PDD), have been defined to meet the objective. They involve: (1) development of new sensitivity analysis methods for RDO and RBDO; (2) development of novel optimization methods for solving RDO problems; (3) development of novel optimization methods for solving RBDO problems; and (4) development of a novel scheme and formulation to solve stochastic design optimization problems with both distributional and structural design parameters. The major achievements are as follows. Firstly, three new computational methods were developed for calculating design sensitivities of statistical moments and reliability of high-dimensional complex systems subject to random inputs. The first method represents a novel integration of PDD of a multivariate stochastic response function and score functions, leading to analytical expressions of design sensitivities of the first two moments. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD-SPA method, entailing the saddlepoint approximation (SPA) and score functions; and the PDD-MCS method, utilizing the embedded Monte Carlo simulation (MCS) of the PDD approximation and score functions. For all three methods developed, both the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Secondly, four new methods were developed for RDO of complex engineering systems. The methods involve PDD of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. The methods, depending on how statistical moment and sensitivity analyses are dovetailed with an optimization algorithm, encompass direct, single-step, sequential, and multi-point single-step design processes. Thirdly, two new methods were developed for RBDO of complex engineering systems. The methods involve an adaptive-sparse polynomial dimensional decomposition (AS-PDD) of a high-dimensional stochastic response for reliability analysis, a novel integration of AS-PDD and score functions for calculating the sensitivities of the failure probability with respect to design variables, and standard gradient-based optimization algorithms, resulting in a multi-point, single-step design process. The two methods, depending on how the failure probability and its design sensitivities are evaluated, exploit two distinct combinations built on AS-PDD: the AS-PDD-SPA method, entailing SPA and score functions; and the AS-PDD-MCS method, utilizing the embedded MCS of the AS-PDD approximation and score functions. In addition, a new method, named as the augmented PDD method, was developed for RDO and RBDO subject to mixed design variables, comprising both distributional and structural design variables. The method comprises a new augmented PDD of a high-dimensional stochastic response for statistical moment and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms, leading to a multi-point, single-step design process. The innovative formulations of statistical moment and reliability analysis, design sensitivity analysis, and optimization algorithms have achieved not only highly accurate but also computationally efficient design solutions. Therefore, these new methods are capable of performing industrial-scale design optimization with numerous design variables.
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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.
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Grill, Maximilian Josef [Verfasser], Wolfgang A. [Akademischer Betreuer] Wall, Wolfgang A. [Gutachter] Wall, and Philipp J. [Gutachter] Thurner. "Computational Models and Methods for Molecular Interactions of Deformable Fibers in Complex Biophysical Systems / Maximilian Josef Grill ; Gutachter: Wolfgang A. Wall, Philipp J. Thurner ; Betreuer: Wolfgang A. Wall." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/122267274X/34.

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Liu, Zifan. "Complex systems and health systems, computational challenges." Thesis, Versailles-St Quentin en Yvelines, 2015. http://www.theses.fr/2015VERS001V/document.

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Le calcul des valeurs propres intervient dans des modèles de maladies d’épidémiques et pourrait être utilisé comme un allié des campagnes de vac- cination dans les actions menées par les organisations de soins de santé. La modélisation épidémique peut être considérée, par analogie, comme celle des viruses d’ordinateur qui dépendent de l’état de graphe sous-jacent à un moment donné. Nous utilisons PageRank comme méthode pour étudier la propagation de l’épidémie et d’envisager son calcul dans le cadre de phé- nomène petit-monde. Une mise en œuvre parallèle de méthode multiple de "implicitly restar- ted Arnoldi method" (MIRAM) est proposé pour calculer le vecteur propre dominant de matrices stochastiques issus de très grands réseaux réels. La grande valeur de "damping factor" pour ce problème fait de nombreux algo- rithmes existants moins efficace, tandis que MIRAM pourrait être promet- teuse. Nous proposons également dans cette thèse un générateur de graphe parallèle qui peut être utilisé pour générer des réseaux synthétisés distri- bués qui présentent des structures "scale-free" et petit-monde. Ce générateur pourrait servir de donnée pour d’autres algorithmes de graphes également. MIRAM est mis en œuvre dans le cadre de trilinos, en ciblant les grandes données et matrices creuses représentant des réseaux sans échelle, aussi connu comme les réseaux de loi de puissance. Hypergraphe approche de partitionnement est utilisé pour minimiser le temps de communication. L’al- gorithme est testé sur un grille national de Grid5000. Les expériences sur les très grands réseaux tels que Twitter et Yahoo avec plus de 1 milliard de nœuds sont exécutées. Avec notre mise en œuvre parallèle, une accélération de 27× est satisfaite par rapport au solveur séquentiel
The eigenvalue equation intervenes in models of infectious disease prop- agation and could be used as an ally of vaccination campaigns in the ac- tions carried out by health care organizations. The epidemiological model- ing techniques can be considered by analogy, as computer viral propagation which depends on the underlying graph status at a given time. We point out PageRank as method to study the epidemic spread and consider its calcula- tion in the context of small-world phenomenon. A parallel implementation of multiple implicitly restarted Arnoldi method (MIRAM) is proposed for calculating dominant eigenpair of stochastic matrices derived from very large real networks. Their high damp- ing factor makes many existing algorithms less efficient, while MIRAM could be promising. We also propose in this thesis a parallel graph gen- erator that can be used to generate distributed synthesized networks that display scale-free and small-world structures. This generator could serve as a testbed for graph related algorithms. MIRAM is implemented within the framework of Trilinos, targeting big data and sparse matrices representing scale-free networks, also known as power law networks. Hypergraph partitioning approach is employed to minimize the communication overhead. The algorithm is tested on a nation wide cluster of clusters Grid5000. Experiments on very large networks such as twitter and yahoo with over 1 billion nodes are conducted. With our parallel implementation, a speedup of 27× is met compared to the sequential solver
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Ahmadi, Adl Amin. "Computational Methods for Biomarker Identification in Complex Disease." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5895.

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In a modern systematic view of biology, cell functions arise from the interaction between molecular components. One of the challenging problems in systems biology with high-throughput measurements is discovering the important components involved in the development and progression of complex diseases, which may serve as biomarkers for accurate predictive modeling and as targets for therapeutic purposes. Due to the non-linearity and heterogeneity of these complex diseases, traditional biomarker identification approaches have had limited success at finding clinically useful biomarkers. In this dissertation we propose novel methods for biomarker identification that explicitly take into account the non-linearity and heterogeneity of complex diseases. We first focus on the methods to deal with non-linearity by taking into account the interactions among features with respect to the disease outcome of interest. We then focus on the methods for finding disease subtypes with their subtype-specific biomarkers for heterogeneous diseases, where we show how prior biological knowledge and simultaneous disease stratification and personalized biomarker identification can help achieve better performance. We develop novel computational methods for more accurate and robust biomarker identification including methods for estimating the interactive effects, a network-based feature ranking algorithm that takes into account the interactive effects between biomarkers, different approaches for finding distances between somatic mutation profiles for better disease stratification using prior knowledge, and a network-regularized bi-clique finding algorithm for simultaneous subtype and biomarker identification. Our experimental results show that our proposed methods perform better than the state-of-the-art methods for both problems.
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Miller, David J. Ghosh Avijit. "New methods in computational systems biology /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2810.

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Gibson, Michael Andrew Bruck Jehoshua. "Computational methods for stochastic biological systems /." Diss., Pasadena, Calif. : California Institute of Technology, 2000. http://resolver.caltech.edu/CaltechETD:etd-05132005-154222.

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Le, Xuan Tuan. "Understanding complex systems through computational modeling and simulation." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEP003.

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Les approches de simulation classiques ne sont en général pas adaptées pour traiter les aspects de complexité que présentent les systèmes complexes tels que l'émergence ou l'adaptation. Dans cette thèse, l'auteur s'appuie sur ses travaux menés dans le cadre d'un projet de simulation sur l’épidémie de grippe en France associée à des interventions sur une population en considérant le phénomène étudié comme un processus diffusif sur un réseau complexe d'individus, l'originalité réside dans le fait que la population y est considérée comme un système réactif. La modélisation de tels systèmes nécessite de spécifier explicitement le comportement des individus et les réactions de ceux-cis tout en produisant un modèle informatique qui doit être à la fois flexible et réutilisable. Les diagrammes d'états sont proposés comme une approche de programmation reposant sur une modélisation validée par l'expertise. Ils correspondent également à une spécification du code informatique désormais disponibles dans les outils logiciels de programmation agent. L'approche agent de type bottom-up permet d'obtenir des simulations de scénario "what-if" où le déroulement des actions peut nécessiter que les agents s'adaptent aux changements de contexte. Cette thèse propose également l'apprentissage pour un agent par l'emploi d'arbre de décision afin d'apporter flexibilité et lisibilité pour la définition du modèle de comportement des agents et une prise de décision adaptée au cours de la simulation. Notre approche de modélisation computationnelle est complémentaire aux approches traditionnelles et peut se révéler indispensable pour garantir une approche pluridisciplinaire validable par l'expertise
Traditional approaches are not sufficient, and sometimes impossible in dealing with complexity issues such as emergence, self-organization, evolution and adaptation of complex systems. As illustrated in this thesis by the practical work of the author in a real-life project, the spreading of infectious disease as well as interventions could be considered as difusion processes on complex networks of heterogeneous individuals in a society which is considered as a reactive system. Modeling of this system requires explicitly specifying of each individual’s behaviors and (re)actions, and transforming them into computational model which has to be flexible, reusable, and ease of coding. Statechart, typical for model-based programming, is a good solution that the thesis proposes. Bottom-up agent based simulation finds emergence episodes in what-if scenarios that change rules governing agent’s behaviors that requires agents to learn to adapt with these changes. Decision tree learning is proposed to bring more flexibility and legibility in modeling of agent’s autonomous decision making during simulation runtime. Our proposition for computational models such as agent based models are complementary to traditional ones, and in some case they are unique solutions due to legal, ethical issues
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Safa, Issam I. "Towards Topological Methods for Complex Scalar Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322457949.

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Books on the topic "Computational methods for Complex Systems"

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M, Mariton, and Abou-Kandil H, eds. Control of complex systems: Methods and technology. New York: Plenum Press, 1991.

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Miller, John H. Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton University Press, 2006.

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Complex systems: Task group summaries : conference, Arnold and Mabel Beckman Center, Irvine, California, November 13-15, 2008. Washington, D.C: National Academies Press, 2009.

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National Academies (U.S.). Keck Futures Initiative. Conference. Complex systems: Task group summaries : conference, Arnold and Mabel Beckman Center, Irvine, California, November 13-15, 2008. Washington, D.C: National Academies Press, 2009.

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Fuchs, Armin. Nonlinear Dynamics in Complex Systems: Theory and Applications for the Life-, Neuro- and Natural Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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E, Page Scott, ed. Complex adaptive systems: An introduction to computational models of social life / John H. Miller and Scott E. Page. Princeton, New Jersey: Princeton University Press, 2007.

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Romania) BICS 2008 (2008 Tîrgu-Mureș. BICS 2008: Proceedings of the 1st International Conference, Bio-inspired Computational Methods Used for Solving Difficult Problems--Development of Intelligent and Complex Systems : Tirgu Mures, Romania, 5-7 November 2008. Edited by Enachescu Calin, Iantovics Barna L, and Filip F. G. Melville, N.Y: American Institute of Physics, 2009.

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Vázquez, Luis. Newtonian Nonlinear Dynamics for Complex Linear and Optimization Problems. New York, NY: Springer New York, 2013.

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Golès, E. Complex Systems. Dordrecht: Springer Netherlands, 2001.

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Mago, Vijay Kumar, and Vahid Dabbaghian, eds. Computational Models of Complex Systems. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01285-8.

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Book chapters on the topic "Computational methods for Complex Systems"

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Heath, J., M. Kwiatkowska, G. Norman, D. Parker, and O. Tymchyshyn. "Probabilistic Model Checking of Complex Biological Pathways." In Computational Methods in Systems Biology, 32–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11885191_3.

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Chaume, Denys, Véronique Giudicelli, Kora Combres, Chantal Ginestoux, and Marie-Paule Lefranc. "IMGT-Choreography: Processing of Complex Immunogenetics Knowledge." In Computational Methods in Systems Biology, 73–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-25974-9_7.

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de Wit, Albert, and Fred van Keulen. "Framework for Multi-Level Optimization of Complex Systems." In Multiscale Methods in Computational Mechanics, 347–77. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9809-2_18.

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Niewiadomska-Szynkiewicz, Ewa. "Parallel Hierarchical Methods for Complex Systems Optimization." In Computational Science and Its Applications - ICCSA 2006, 537–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751540_57.

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Zhang, Shihua. "Network Analysis, Integration and Methods in Computational Biology: A Brief Survey on Recent Advances." In Complex Systems and Networks, 459–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47824-0_18.

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Özen, Figen, and Dilek Bilgin Tükel. "Robotic Dance Modeling Methods." In Complex Systems: Spanning Control and Computational Cybernetics: Applications, 35–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00978-5_2.

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Cașcaval, Petru, and Florin Leon. "Active Redundancy Allocation in Complex Systems by Using Different Optimization Methods." In Computational Collective Intelligence, 625–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28377-3_52.

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Iordache, Dan Alexandru, and Paul Enache Sterian. "Study of Some Complex Systems by Using Numerical Methods." In Computational Science and Its Applications – ICCSA 2018, 539–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95165-2_38.

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Kämmerer, Lutz, Stefan Kunis, Ines Melzer, Daniel Potts, and Toni Volkmer. "Computational Methods for the Fourier Analysis of Sparse High-Dimensional Functions." In Extraction of Quantifiable Information from Complex Systems, 347–63. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08159-5_17.

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Masui, Koki, Masao Ogino, and Lijun Liu. "Multiple-Precision Iterative Methods for Solving Complex Symmetric Electromagnetic Systems." In Lecture Notes in Computational Science and Engineering, 321–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30705-9_28.

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Conference papers on the topic "Computational methods for Complex Systems"

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Tinnirello, Alicia María, Eduardo Alberto Gago, and Matías Francisco Romero. "COMPUTATIONAL METHODS: THEIR ADVANTAGES ON TEACHING COMPLEX FLUID FLOW SYSTEMS." In 12th International Technology, Education and Development Conference. IATED, 2018. http://dx.doi.org/10.21125/inted.2018.1734.

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Altoè, Piero, Marco Stenta, Andrea Bottoni, Marco Garavelli, George Maroulis, and Theodore E. Simos. "COBRAMM: A Tunable QM∕MM Approach to Complex Molecular Architectures. Modelling the Excited and Ground State Properties of Sized Molecular Systems." In Computational Methods in Science and Engineering. AIP, 2007. http://dx.doi.org/10.1063/1.2827033.

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Mikes, Ioannis G., and Andreas J. Kappos. "SIMPLE AND COMPLEX MODELLING OF SEAT-TYPE ABUTMENT-BACKFILL SYSTEMS." In 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering Methods in Structural Dynamics and Earthquake Engineering. Athens: Institute of Structural Analysis and Antiseismic Research National Technical University of Athens, 2021. http://dx.doi.org/10.7712/120121.8864.19520.

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Blekhman, I. "MULTIMODE CHARACTER OF DYNAMICAL SYSTEMS AS A CAUSE OF THEIR COMPLEX (“CHAOTIC”) BEHAVIOR." In 4th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2014. http://dx.doi.org/10.7712/120113.4656.c1432.

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Chen, Chen, Changtong Luo, and Zonglin Jiang. "Fast Modeling Methods for Complex System with Separable Features." In 2017 10th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2017. http://dx.doi.org/10.1109/iscid.2017.144.

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Iantovics, Barna, Călin Enăchescu, Barna Laszlo Iantovics, Enachescu Calin, and Florin Gheorghe Filip. "Intelligent Complex Evolutionary Agent-Based Systems." In BICS 2008: Proceedings of the 1st International Conference on Bio-Inspired Computational Methods Used for Difficult Problems Solving: Development of Intelligent and Complex Systems. AIP, 2009. http://dx.doi.org/10.1063/1.3130613.

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Harmon, S. Y. "A Method For Estimating Computational Requirements For Complex Robots." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems. SPIE, 1990. http://dx.doi.org/10.1117/12.969877.

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Rusin, Jarosław. "FLEXIBLE COMPLEX SYSTEM OF A DOUBLE-STRING UNDER EXTREME MOVING LOADS." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2210.8686.

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Baraille, Isabelle, and Didier Bégué. "Preface of the "Symposium on theoretical and experimental coupling: On the treatment of complex chemical systems"." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2014 (ICCMSE 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4897803.

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Murthy, Jayathi Y. "Computational Heat Transfer in Complex Systems: A Review of Needs and Opportunities." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-23367.

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During the few decades, computational techniques for simulating heat transfer in complex industrial systems have reached maturity. Combined with increasingly sophisticated modeling of turbulence, chemistry, radiation, phase change and other physics, powerful computational fluid dynamics (CFD) and computational heat transfer (CHT) solvers have been developed which are beginning to enter the industrial design cycle. In this paper, an overview of emerging simulation needs is first given, and currently-available CFD techniques are evaluated in light of these needs. Emerging computational methods which address some of the failings of current techniques are then reviewed. New research opportunities for computational heat transfer, such as in sub-micron and multiscale heat transport, are reviewed. As computational techniques and physical models become mature, there is increasing demand for predictive simulation, that is, simulation which is not only verified and validated, but whose uncertainty is also quantified. Current work in the area of sensitivity computation and uncertainty propagation is described.
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Reports on the topic "Computational methods for Complex Systems"

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Cai, Wei. Numerical Methods of Computational Electromagnetics for Complex Inhomogeneous Systems. Office of Scientific and Technical Information (OSTI), May 2014. http://dx.doi.org/10.2172/1131316.

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Patel, Reena, David Thompson, Guillermo Riveros, Wayne Hodo, John Peters, and Felipe Acosta. Dimensional analysis of structural response in complex biological structures. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41082.

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The solution to many engineering problems is obtained through the combination of analytical, computational and experimental methods. In many cases, cost or size constraints limit testing of full-scale articles. Similitude allows observations made in the laboratory to be used to extrapolate the behavior to full-scale system by establishing relationships between the results obtained in a scaled experiment and those anticipated for the full-scale prototype. This paper describes the application of the Buckingham Pi theorem to develop a set of non-dimensional parameters that are appropriate for describing the problem of a distributed load applied to the rostrum of the paddlefish. This problem is of interest because previous research has demonstrated that the rostrum is a very efficient structural system. The ultimate goal is to estimate the response of a complex, bio-inspired structure based on the rostrum to blast load. The derived similitude laws are verified through a series of numerical experiments having a maximum error of 3.39%.
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Murman, Earll M., and Judson R. Baron. Computational Methods for Complex Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada172727.

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Soloviev, Vladimir N., Andrii O. Bielinskyi, and Natalia A. Kharadzjan. Coverage of the Coronavirus Pandemic through Entropy Measures. CEUR Workshop Proceedings, March 2021. http://dx.doi.org/10.31812/123456789/4427.

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The rapidly evolving coronavirus pandemic brings a devastating effect on the entire world and its economy as awhole. Further instability related to COVID-19will negatively affect not only on companies and financial markets, but also on traders and investors that have been interested in saving their investment, minimizing risks, and making decisions such as how to manage their resources, how much to consume and save, when to buy or sell stocks, etc., and these decisions depend on the expectation of when to expect next critical change. Trying to help people in their subsequent decisions, we demonstrate the possibility of constructing indicators of critical and crash phenomena on the example of Bitcoin market crashes for further demonstration of their efficiency on the crash that is related to the coronavirus pandemic. For this purpose, the methods of the theory of complex systems have been used. Since the theory of complex systems has quite an extensive toolkit for exploring the nonlinear complex system, we take a look at the application of the concept of entropy in finance and use this concept to construct 6 effective entropy measures: Shannon entropy, Approximate entropy, Permutation entropy, and 3 Recurrence based entropies. We provide computational results that prove that these indicators could have been used to identify the beginning of the crash and predict the future course of events associated with the current pandemic.
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Noor, Ahmed K. Effective Computational Strategy for Predicting the Response of Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada277452.

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Trenchea, Catalin, and William Layton. Computational Methods for Predictive Simulation of Stochastic Turbulence Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2015. http://dx.doi.org/10.21236/ada627253.

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Jameson, Anthony, and Juan J. Alonso. Computational Algorithms for High-Fidelity Multidisciplinary Design of Complex Aerospace Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada430007.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Willcox, K., D. Allaire, J. Deyst, C. He, and G. Sondecker. Stochastic Process Decision Methods for Complex-Cyber-Physical Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada552217.

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Burns, John A., Eugene M. Cliff, and Lizette Zietsman. Computational Methods for Identification, Optimization and Control of PDE Systems. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada523367.

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