Literatura científica selecionada sobre o tema "Dynamical system modeling"
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Artigos de revistas sobre o assunto "Dynamical system modeling"
Bors, Dorota, e Robert Stańczy. "Dynamical system modeling fermionic limit". Discrete & Continuous Dynamical Systems - B 23, n.º 1 (2018): 45–55. http://dx.doi.org/10.3934/dcdsb.2018004.
Texto completo da fonteDmitriev, Andrey, Olga Tsukanova e Svetlana Maltseva. "Modeling of Microblogging Social Networks: Dynamical System vs. Random Dynamical System". Procedia Computer Science 122 (2017): 812–19. http://dx.doi.org/10.1016/j.procs.2017.11.441.
Texto completo da fonteJANSSON, JOHAN, CLAES JOHNSON e ANDERS LOGG. "COMPUTATIONAL MODELING OF DYNAMICAL SYSTEMS". Mathematical Models and Methods in Applied Sciences 15, n.º 03 (março de 2005): 471–81. http://dx.doi.org/10.1142/s0218202505000431.
Texto completo da fonteRunolfsson, Thordur. "Towards hybrid system modeling of uncertain complex dynamical systems". Nonlinear Analysis: Hybrid Systems 2, n.º 2 (junho de 2008): 383–93. http://dx.doi.org/10.1016/j.nahs.2006.05.004.
Texto completo da fonteRedondo, J. M., D. Ibarra-Vega, J. Catumba-Ruíz e M. P. Sánchez-Muñoz. "Hydrological system modeling: Approach for analysis with dynamical systems". Journal of Physics: Conference Series 1514 (março de 2020): 012013. http://dx.doi.org/10.1088/1742-6596/1514/1/012013.
Texto completo da fonteFrankel, Michael L., Gregor Kovačič, Victor Roytburd e Ilya Timofeyev. "Finite-dimensional dynamical system modeling thermal instabilities". Physica D: Nonlinear Phenomena 137, n.º 3-4 (março de 2000): 295–315. http://dx.doi.org/10.1016/s0167-2789(99)00180-3.
Texto completo da fonteNasim, Imran, e Michael E. Henderson. "Dynamically Meaningful Latent Representations of Dynamical Systems". Mathematics 12, n.º 3 (2 de fevereiro de 2024): 476. http://dx.doi.org/10.3390/math12030476.
Texto completo da fonteABEL, MARKUS. "NONPARAMETRIC MODELING AND SPATIOTEMPORAL DYNAMICAL SYSTEMS". International Journal of Bifurcation and Chaos 14, n.º 06 (junho de 2004): 2027–39. http://dx.doi.org/10.1142/s0218127404010382.
Texto completo da fonteJian, Shen, Han Feng, Chen Fang, Zhou Qiao e Pavel M. Trivailo. "Dynamics and modeling of rocket towed net system". Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, n.º 1 (13 de outubro de 2016): 185–97. http://dx.doi.org/10.1177/0954410016673090.
Texto completo da fonteHahn, Luzia, e Peter Eberhard. "Transient Dynamical-Thermal-Optical System Modeling and Simulation". EPJ Web of Conferences 238 (2020): 12001. http://dx.doi.org/10.1051/epjconf/202023812001.
Texto completo da fonteTeses / dissertações sobre o assunto "Dynamical system modeling"
Kawashima, Hiroaki. "Interval-Based Hybrid Dynamical System for Modeling Dynamic Events and Structures". 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/68896.
Texto completo da fonteFRANCH, Daniel Kudlowiez. "Dynamical system modeling with probabilistic finite state automata". Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25448.
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FACEPE
Discrete dynamical systems are widely used in a variety of scientific and engineering applications, such as electrical circuits, machine learning, meteorology and neurobiology. Modeling these systems involves performing statistical analysis of the system output to estimate the parameters of a model so it can behave similarly to the original system. These models can be used for simulation, performance analysis, fault detection, among other applications. The current work presents two new algorithms to model discrete dynamical systems from two categories (synchronizable and non-synchronizable) using Probabilistic Finite State Automata (PFSA) by analyzing discrete symbolic sequences generated by the original system and applying statistical methods and inference, machine learning algorithms and graph minimization techniques to obtain compact, precise and efficient PFSA models. Their performance and time complexity are compared with other algorithms present in literature that aim to achieve the same goal by applying the algorithms to a series of common examples.
Sistemas dinâmicos discretos são amplamente usados em uma variedade de aplicações cientifícas e de engenharia, por exemplo, circuitos elétricos, aprendizado de máquina, meteorologia e neurobiologia. O modelamento destes sistemas envolve realizar uma análise estatística de sequências de saída do sistema para estimar parâmetros de um modelo para que este se comporte de maneira similar ao sistema original. Esses modelos podem ser usados para simulação, referência ou detecção de falhas. Este trabalho apresenta dois novos algoritmos para modelar sistemas dinâmicos discretos de duas categorias (sincronizáveis e não-sincronizáveis) por meio de Autômatos Finitos Probabilísticos (PFSA, Probabilistic Finite State Automata) analisando sequências geradas pelo sistema original e aplicando métodos estatísticos, algoritmos de aprendizado de máquina e técnicas de minimização de grafos para obter modelos PFSA compactos e eficientes. Sua performance e complexidade temporal são comparadas com algoritmos presentes na literatura que buscam atingir o mesmo objetivo aplicando os algoritmos a uma série de exemplos.
Liu, Chunmeni 1970. "Dynamical system modeling of a micro gas turbine engine". Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9249.
Texto completo da fonteAlso available online at the MIT Theses Online homepage
Includes bibliographical references (p. 123).
Since 1995, MIT has been developing the technology for a micro gas turbine engine capable of producing tens of watts of power in a package less than one cubic centimeter in volume. The demo engine developed for this research has low and diabtic component performance and severe heat transfer from the turbine side to the compressor side. The goals of this thesis are developing a dynamical model and providing a simulation platform for predicting the microengine performance and control design, as well as giving an estimate of the microengine behavior under current design. The thesis first analyzes and models the dynamical components of the microengine. Then a nonlinear model, a linearized model, and corresponding simulators are derived, which are valid for estimating both the steady state and transient behavior. Simulations are also performed to estimate the microengine performance, which include steady states, linear properties, transient behavior, and sensor options. A parameter study and investigation of the startup process are also performed. Analysis and simulations show that there is the possibility of increasing turbine inlet temperature with decreasing fuel flow rate in some regions. Because of the severe heat transfer and this turbine inlet temperature trend, the microengine system behaves like a second-order system with low damping and poor linear properties. This increases the possibility of surge, over-temperature and over-speed. This also implies a potentially complex control system. The surge margin at the design point is large, but accelerating directly from minimum speed to 100% speed still causes surge. Investigation of the sensor options shows that temperature sensors have relatively fast response time but give multiple estimates of the engine state. Pressure sensors have relatively slow response time but they change monotonically with the engine state. So the future choice of sensors may be some combinations of the two. For the purpose of feedback control, the system is observable from speed, temperature, or pressure measurements. Parameter studies show that the engine performance doesn't change significantly with changes in either nozzle area or the coefficient relating heat flux to compressor efficiency. It does depend strongly on the coefficient relating heat flux to compressor pressure ratio. The value of the compressor peak efficiency affects the engine operation only when it is inside the range of the engine operation. Finally, parameter studies indicate that, to obtain improved transient behavior with less possibility of surge, over-temperature and over-speed, and to simplify the system analysis and design as well as the design and implementation of control laws, it is desirable to reduce the ratio of rotor mechanical inertia to thermal inertia, e.g. by slowing the thermal dynamics. This can in some cases decouple the dynamics of rotor acceleration and heat transfer. Several methods were shown to improve the startup process: higher start speed, higher start spool temperature, and higher start fuel flow input. Simulations also show that the efficiency gradient affects the transient behavior of the engine significantly, thereby effecting the startup process. Finally, the analysis and modeling methodologies presented in this thesis can be applied to other engines with severe heat transfer. The estimates of the engine performance can serve as a reference of similar engines as well.
by Chunmei Liu.
S.M.
Hsiao, Yu-Chung Ph D. Massachusetts Institute of Technology. "Automated modeling of nonlinear dynamical subsystems for stable system simulation". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99828.
Texto completo da fonteCataloged from PDF version of thesis.
Includes bibliographical references (pages 107-113).
Automated modeling techniques allow fast prototyping from measurement or simulation data and can facilitate many important application scenarios, for instance, shortening the time frame from subsystem design to system integration, calibrating models with higher-order effects, and providing protected models without revealing the intellectual properties of actual designs. Many existing techniques can generate nonlinear dynamical models that are stable when simulated alone. However, such generated models oftentimes result in unstable simulation when interconnected within a physical network. This is because energy-related system properties are not properly enforced, and the generated models erroneously produce numerical energy, which in turn causes instability of the entire physical network. Therefore, when modeling a system that is unable to generate energy, it is essential to enforce passivity in order to ensure stable system simulation. This thesis presents an algorithm that can automatically generate nonlinear passive dynamical models via convex optimization. Convex constraints are proposed to guarantee model passivity and incremental stability. The generated nonlinear models are suited to be interconnected within physical networks in order to enable the hierarchical modeling strategy. Practical examples include circuit networks and arterial networks. It is demonstrated that our generated models, when interconnected within a system, can be simulated in a numerically stable way. The system dynamics of the interconnected models can be faithfully reproduced for a range of operations and show an excellent agreement with a number of system metrics. In addition, it is also shown via these two applications that the proposed modeling technique is applicable to multiple physical domains.
by Yu-Chung Hsiao.
Ph. D.
Mattos, César Lincoln Cavalcante. "Recurrent gaussian processes and robust dynamical modeling". reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/25604.
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The study of dynamical systems is widespread across several areas of knowledge. Sequential data is generated constantly by different phenomena, most of them we cannot explain by equations derived from known physical laws and structures. In such context, this thesis aims to tackle the task of nonlinear system identification, which builds models directly from sequential measurements. More specifically, we approach challenging scenarios, such as learning temporal relations from noisy data, data containing discrepant values (outliers) and large datasets. In the interface between statistics, computer science, data analysis and engineering lies the machine learning community, which brings powerful tools to find patterns from data and make predictions. In that sense, we follow methods based on Gaussian Processes (GP), a principled, practical, probabilistic approach to learning in kernel machines. We aim to exploit recent advances in general GP modeling to bring new contributions to the dynamical modeling exercise. Thus, we propose the novel family of Recurrent Gaussian Processes (RGPs) models and extend their concept to handle outlier-robust requirements and scalable stochastic learning. The hierarchical latent (non-observed) structure of those models impose intractabilities in the form of non-analytical expressions, which are handled with the derivation of new variational algorithms to perform approximate deterministic inference as an optimization problem. The presented solutions enable uncertainty propagation on both training and testing, with focus on free simulation. We comprehensively evaluate the proposed methods with both artificial and real system identification benchmarks, as well as other related dynamical settings. The obtained results indicate that the proposed approaches are competitive when compared to the state of the art in the aforementioned complicated setups and that GP-based dynamical modeling is a promising area of research.
O estudo dos sistemas dinâmicos encontra-se disseminado em várias áreas do conhecimento. Dados sequenciais são gerados constantemente por diversos fenômenos, a maioria deles não passíveis de serem explicados por equações derivadas de leis físicas e estruturas conhecidas. Nesse contexto, esta tese tem como objetivo abordar a tarefa de identificação de sistemas não lineares, por meio da qual são obtidos modelos diretamente a partir de observações sequenciais. Mais especificamente, nós abordamos cenários desafiadores, tais como o aprendizado de relações temporais a partir de dados ruidosos, dados contendo valores discrepantes (outliers) e grandes conjuntos de dados. Na interface entre estatísticas, ciência da computação, análise de dados e engenharia encontra-se a comunidade de aprendizagem de máquina, que fornece ferramentas poderosas para encontrar padrões a partir de dados e fazer previsões. Nesse sentido, seguimos métodos baseados em Processos Gaussianos (PGs), uma abordagem probabilística prática para a aprendizagem de máquinas de kernel. A partir de avanços recentes em modelagem geral baseada em PGs, introduzimos novas contribuições para o exercício de modelagem dinâmica. Desse modo, propomos a nova família de modelos de Processos Gaussianos Recorrentes (RGPs, da sigla em inglês) e estendemos seu conceito para lidar com requisitos de robustez a outliers e aprendizagem estocástica escalável. A estrutura hierárquica e latente (não-observada) desses modelos impõe expressões não- analíticas, que são resolvidas com a derivação de novos algoritmos variacionais para realizar inferência determinista aproximada como um problema de otimização. As soluções apresentadas permitem a propagação da incerteza tanto no treinamento quanto no teste, com foco em realizar simulação livre. Nós avaliamos em detalhe os métodos propostos com benchmarks artificiais e reais da área de identificação de sistemas, assim como outras tarefas envolvendo dados dinâmicos. Os resultados obtidos indicam que nossas propostas são competitivas quando comparadas ao estado da arte, mesmo nos cenários que apresentam as complicações supracitadas, e que a modelagem dinâmica baseada em PGs é uma área de pesquisa promissora.
Erdogan, Ezgi. "A Complex Dynamical Systems Model Of Education, Research, Employment, And Sustainable Human Development". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12612138/index.pdf.
Texto completo da fonteWang, Chiying. "Contributions to Collective Dynamical Clustering-Modeling of Discrete Time Series". Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/198.
Texto completo da fonteAnderson, James David. "Dynamical system decomposition and analysis using convex optimization". Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:624001be-28d5-4837-a7d8-2222e270e658.
Texto completo da fonteXie, Junfei. "Data-Driven Decision-Making Framework for Large-Scale Dynamical Systems under Uncertainty". Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862845/.
Texto completo da fonteYin, Yuan. "Physics-Aware Deep Learning and Dynamical Systems : Hybrid Modeling and Generalization". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS161.
Texto completo da fonteDeep learning has made significant progress in various fields and has emerged as a promising tool for modeling physical dynamical phenomena that exhibit highly nonlinear relationships. However, existing approaches are limited in their ability to make physically sound predictions due to the lack of prior knowledge and to handle real-world scenarios where data comes from multiple dynamics or is irregularly distributed in time and space. This thesis aims to overcome these limitations in the following directions: improving neural network-based dynamics modeling by leveraging physical models through hybrid modeling; extending the generalization power of dynamics models by learning commonalities from data of different dynamics to extrapolate to unseen systems; and handling free-form data and continuously predicting phenomena in time and space through continuous modeling. We highlight the versatility of deep learning techniques, and the proposed directions show promise for improving their accuracy and generalization power, paving the way for future research in new applications
Livros sobre o assunto "Dynamical system modeling"
Fuchs, Hans U. Modeling of uniform dynamical systems: A system dynamics approach. Zürich: Füssli, 2002.
Encontre o texto completo da fonteGoebel, Rafal. Hybrid dynamical systems: Modeling, stability, and robustness. Princeton, N.J: Princeton University Press, 2012.
Encontre o texto completo da fontePalm, William J. Modeling, analysis, and control of dynamic systems. 2a ed. New York: Wiley, 1998.
Encontre o texto completo da fontePalm, William J. Modeling, analysis, and control of dynamic systems. 2a ed. New York: Wiley, 1999.
Encontre o texto completo da fonteMukherjee, Animesh. Dynamics On and Of Complex Networks, Volume 2: Applications to Time-Varying Dynamical Systems. New York, NY: Springer New York, 2013.
Encontre o texto completo da fonteAbarbanel, Henry. Predicting the Future: Completing Models of Observed Complex Systems. New York, NY: Springer New York, 2013.
Encontre o texto completo da fonteBeltrami, Edward J. Mathematics for dynamic modeling. Boston: Academic Press, 1987.
Encontre o texto completo da fonteBeltrami, Edward J. Mathematics for dynamic modeling. 2a ed. Boston: Academic Press, 1998.
Encontre o texto completo da fonteAwrejcewicz, Jan, ed. Dynamical Systems: Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42402-6.
Texto completo da fonteL, Margolis Donald, e Rosenberg Ronald C, eds. System dynamics: Modeling and simulation of mechatronic systems. 3a ed. New York: Wiley, 2000.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Dynamical system modeling"
Fox, William P. "Discrete Dynamical System Models". In Mathematical Modeling for Business Analytics, 247–306. Boca Raton, FL : CRC Press, 2018.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315150208-7.
Texto completo da fonteFox, William P. "Mathematics of Finance with Discrete Dynamical System". In Mathematical Modeling for Business Analytics, 335–75. Boca Raton, FL : CRC Press, 2018.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315150208-9.
Texto completo da fonteAxenides, M., E. Floratos, D. Katsinis e G. Linardopoulos. "M-Theory as a Dynamical System Generator". In 13th Chaotic Modeling and Simulation International Conference, 73–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70795-8_6.
Texto completo da fonteRoyer, J. F. "The GCM as a Dynamical System". In Numerical Modeling of the Global Atmosphere in the Climate System, 29–58. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4046-1_2.
Texto completo da fonteBoutalis, Yiannis, Dimitrios Theodoridis, Theodore Kottas e Manolis A. Christodoulou. "Identification of Dynamical Systems Using Recurrent Neurofuzzy Modeling". In System Identification and Adaptive Control, 25–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06364-5_2.
Texto completo da fonteAvşar, Ahmet Levent, İstek Tatar e Cihangir Duran. "Dynamical Modeling and Verification of Underwater Acoustic System". In Topics in Model Validation and Uncertainty Quantification, Volume 5, 255–63. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6564-5_24.
Texto completo da fonteChmielewski, Adrian, Jakub Możaryn, Robert Gumiński, Krzysztof Bogdziński e Przemysław Szulim. "Experimental Evaluation of Mathematical and Artificial Neural Network Modeling of Energy Storage System". In Dynamical Systems in Applications, 49–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96601-4_5.
Texto completo da fonteAntoniou, Stathis. "A Dynamical System Modeling Solid 2-Dimensional 0-Surgery". In Mathematical Modeling Through Topological Surgery and Applications, 41–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97067-7_7.
Texto completo da fonteCoolen, Anthony C. C., Theodore Nikoletopoulos, Shunta Arai e Kazuyuki Tanaka. "Dynamical Analysis of Quantum Annealing". In Sublinear Computation Paradigm, 295–317. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4095-7_12.
Texto completo da fonteLi, Guoshi, e Thomas A. Cleland. "Generative Biophysical Modeling of Dynamical Networks in the Olfactory System". In Methods in Molecular Biology, 265–88. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8609-5_20.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Dynamical system modeling"
Kuhlman, Chris J., V. S. Anil Kumar, Madhav V. Marathe, Henning S. Mortveit, Samarth Swarup, Gaurav Tuli, S. S. Ravi e Daniel J. Rosenkrantz. "A general-purpose graph dynamical system modeling framework". In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6147758.
Texto completo da fonteLee, Chan-Su. "Human Action Recognition Using Tensor Dynamical System Modeling". In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.242.
Texto completo da fonteSaadi de Almeida Lettieri, Davi, e Leonardo Santos de Brito Alves. "Modeling a dynamical system from low-sampled data". In 27th Brazilian Congress of Thermal Sciences and Engineering. ABCM, 2023. http://dx.doi.org/10.26678/abcm.cobem2023.cob2023-1366.
Texto completo da fonteYilmaz, Sevcan, e Yusuf Oysal. "Dynamic fuzzy system design for modeling and control of nonlinear dynamical processes". In 2015 Science and Information Conference (SAI). IEEE, 2015. http://dx.doi.org/10.1109/sai.2015.7237183.
Texto completo da fonteLuo, Yang, Natalie Baddour e Ming Liang. "Dynamical Modeling of Gear Transmission Considering Gearbox Casing". In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85656.
Texto completo da fonteHadizadeh, Ehsan, e Kourosh H. Shirazi. "Dynamical Modeling for Improvement of Water Treatment System using Bond Graph Method". In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.735-088.
Texto completo da fonteYang, Yejiang, e Weiming Xiang. "Modeling Dynamical Systems with Neural Hybrid System Framework via Maximum Entropy Approach". In 2023 American Control Conference (ACC). IEEE, 2023. http://dx.doi.org/10.23919/acc55779.2023.10155820.
Texto completo da fonteDiaz-Saldierna, L. H., D. Langarica-Cordoba, J. Leyva-Ramos e J. A. Morales-Saldana. "Dynamical modeling for a fuel-cell based power generation system". In 2016 IEEE International Conference on Automatica (ICA-ACCA). IEEE, 2016. http://dx.doi.org/10.1109/ica-acca.2016.7778478.
Texto completo da fonteZhang Juncai. "Dynamic task studying of Multi-intelligence Agents based on dynamical node model in cold forming". In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620471.
Texto completo da fonteRozenberg, Valerii L'vovich. "Dynamical reconstruction of inputs in a stochastic diffusion system". In International conference "Systems Analysis: Modeling and Control" in memory of Academician A. V. Kryazhimskiy. Moscow: Steklov Mathematical Institute, 2018. http://dx.doi.org/10.4213/proc20604.
Texto completo da fonteRelatórios de organizações sobre o assunto "Dynamical system modeling"
Perdigão, Rui A. P., e Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, novembro de 2020. http://dx.doi.org/10.46337/201111.
Texto completo da fonteBishop, A., P. Lomdahl, N. G. Jensen, D. S. Cai, F. Mertenz, Hidetoshi Konno e M. Salkola. Modeling mesoscopic phenomena in extended dynamical systems. Office of Scientific and Technical Information (OSTI), agosto de 1997. http://dx.doi.org/10.2172/522274.
Texto completo da fonteMatei, Ion, e Conrad E. Bock. Modeling Methodologies and Simulation for Dynamical Systems. National Institute of Standards and Technology, agosto de 2012. http://dx.doi.org/10.6028/nist.ir.7875.
Texto completo da fonteВодолєєва, І. С., А. О. Лазаренко e В. М. Соловйов. Дослідження стійкості мультиплексних мереж під час кризових явищ. Видавець Вовчок О.Ю., 2017. http://dx.doi.org/10.31812/0564/1259.
Texto completo da fontePerdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, outubro de 2021. http://dx.doi.org/10.46337/211021.
Texto completo da fonteMcDevitt, Michael E. System Dynamics Aviation Readiness Modeling Demonstration. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2005. http://dx.doi.org/10.21236/ada436605.
Texto completo da fonteAnderson, Ed, Nazli Choucri, Daniel Goldsmith, Stuart E. Madnick, Michael Siegel e Dan Sturtevant. System Dynamics Modeling for Proactive Intelligence. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2010. http://dx.doi.org/10.21236/ada514594.
Texto completo da fonteRicca, Bernard. Introduction to Nonlinear Dynamical Systems and Analysis. Instats Inc., 2024. http://dx.doi.org/10.61700/j16tr1vnie1lu1801.
Texto completo da fonteEquihua, M., e O. Perez-Maqueo. Mathematical Modeling and Conservation. American Museum of Natural History, 2010. http://dx.doi.org/10.5531/cbc.ncep.0154.
Texto completo da fonteBrockett, R. W. Modeling and Estimation Theory for Stochastic Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, setembro de 1986. http://dx.doi.org/10.21236/ada172902.
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