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Artigos de revistas sobre o assunto "Uncertainty propagation in a dynamical context"
Raïssi, Tarek, e Denis Efimov. "Some recent results on the design and implementation of interval observers for uncertain systems". at - Automatisierungstechnik 66, n.º 3 (26 de março de 2018): 213–24. http://dx.doi.org/10.1515/auto-2017-0081.
Texto completo da fonteMartins, L. L., J. P. Gomes e A. S. Ribeiro. "Metrological quality of the excitation force in forced vibration test of concrete dams". Journal of Physics: Conference Series 2647, n.º 21 (1 de junho de 2024): 212001. http://dx.doi.org/10.1088/1742-6596/2647/21/212001.
Texto completo da fonteMezić, Igor, e Thordur Runolfsson. "Uncertainty propagation in dynamical systems". Automatica 44, n.º 12 (dezembro de 2008): 3003–13. http://dx.doi.org/10.1016/j.automatica.2008.04.020.
Texto completo da fonteBanks, H. T., e Shuhua Hu. "Propagation of Uncertainty in Dynamical Systems". Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2012 (5 de maio de 2012): 134–39. http://dx.doi.org/10.5687/sss.2012.134.
Texto completo da fontePiqueira, José R. C., e Felipe Barbosa Cesar. "Dynamical Models for Computer Viruses Propagation". Mathematical Problems in Engineering 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/940526.
Texto completo da fonteDeMars, Kyle J., Robert H. Bishop e Moriba K. Jah. "Entropy-Based Approach for Uncertainty Propagation of Nonlinear Dynamical Systems". Journal of Guidance, Control, and Dynamics 36, n.º 4 (julho de 2013): 1047–57. http://dx.doi.org/10.2514/1.58987.
Texto completo da fontePark, Inkwan, Kohei Fujimoto e Daniel J. Scheeres. "Effect of Dynamical Accuracy for Uncertainty Propagation of Perturbed Keplerian Motion". Journal of Guidance, Control, and Dynamics 38, n.º 12 (dezembro de 2015): 2287–300. http://dx.doi.org/10.2514/1.g000956.
Texto completo da fonteXu, Tianlai, Zhe Zhang e Hongwei Han. "Adaptive Gaussian Mixture Model for Uncertainty Propagation Using Virtual Sample Generation". Applied Sciences 13, n.º 5 (27 de fevereiro de 2023): 3069. http://dx.doi.org/10.3390/app13053069.
Texto completo da fonteBaili, H., e G. A. Fleury. "Indirect Measurement Within Dynamical Context: Probabilistic Approach to Deal With Uncertainty". IEEE Transactions on Instrumentation and Measurement 53, n.º 6 (dezembro de 2004): 1449–54. http://dx.doi.org/10.1109/tim.2004.831138.
Texto completo da fonteKuehn, Christian. "Uncertainty transformation via Hopf bifurcation in fast–slow systems". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, n.º 2200 (abril de 2017): 20160346. http://dx.doi.org/10.1098/rspa.2016.0346.
Texto completo da fonteTeses / dissertações sobre o assunto "Uncertainty propagation in a dynamical context"
Hernandez-Sabio, Sylvain. "Contribution à la métrologie des faibles forces : traçabilité des mesures dynamiques par inversion ensembliste". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD058.
Texto completo da fonteThis PhD thesis is a contribution to small force metrology, in line with the research activities carried out in the AS2M department of the FEMTO-ST institute. This manuscript presents the design and experimental implementation of a triaxial pendulous accelerometer, which measures the unfiltered seismic activity, since the latter is likely to interfere with the operation of an electromagnetic micro-nanoforce balance currently under development. An alternative methodology is also proposed in this manuscript to specifically estimate the value and uncertainty associated with one or more unknown quantities of interest, using a dynamical SISO system whose behavior is uncertain and disturbed. This approach is based on the exact representation of this system by means of a virtual corrective input containing the quantities of interest. This input is estimated and then shaped to determine the uncertainty associated with these quantities of interest, using the tools of interval analysis. The proposed methodology is validated on the basis of simulated accelerometer responses in active and passive modes, then illustrated on the experimental setup. A simulation study of the coupled operation of the future electromagnetic micro-nanoforce balance with the triaxial accelerometer is also carried out. The proposed approach is implemented in a simulated test aiming at characterizing the mechanical stiffness of an elastic cantilever
Kundu, Abhishek. "Efficient uncertainty propagation schemes for dynamical systems with stochastic finite element analysis". Thesis, Swansea University, 2014. https://cronfa.swan.ac.uk/Record/cronfa42292.
Texto completo da fontePerrin, Guillaume. "Random fields and associated statistical inverse problems for uncertainty quantification : application to railway track geometries for high-speed trains dynamical responses and risk assessment". Phd thesis, Université Paris-Est, 2013. http://pastel.archives-ouvertes.fr/pastel-01001045.
Texto completo da fonteAudinot, Timothée. "Développement d’un modèle de dynamique forestière à grande échelle pour simuler les forêts françaises dans un contexte non-stationnaire". Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0179.
Texto completo da fonteContext. Since the industrial revolution, European forests have shown expansion of their area and growing stock. This expansion, together with climate change, drive changes in the processes of forest dynamic. The emergence of a European bioeconomy strategy suggests new developments of forest management strategies at European and national levels. Simulating future forest resources and their management with large-scale models is therefore essential to provide strategic planning support tools. In France, forest resources show high diversity as compared with other European countries' forests. The MARGOT forest dynamic model (MAtrix model of forest Resource Growth and dynamics On the Territory scale), was developed by the national forest inventory (IFN) in 1993 to simulate French forest resources from data of this inventory, but has been the subject of restricted developments, and simulations remain limited to a time horizon shorter than 30 years, under “business as usual” management scenarios, and not taking into account non-stationary forest and environmental contexts.Aims. The general ambition of this thesis was to consent a significant development effort on MARGOT model, in order to tackle current forestry issues. The specific objectives were: i) to assess the capacity of MARGOT to describe French forest expansion over a long retrospective period (1971-2016), ii) to take into account the heterogeneity of forests at large-scale in a holistic way, iii) to account for the impacts of forest densification in demographic dynamic processes, iv) to encompass external climatic forcing in forest growth, v) in a very uncertain context, to be able to quantify NFI sampling uncertainty in model parameters and simulations with respect to the magnitude of other trends considered. The development of forest management scenarios remained outside the scope of this work.Main results. A generic method for forest partitioning according to their geographic and compositional heterogeneity has been implemented. This method is intended to be applied to other European forest contexts. A method of propagating sampling uncertainty to model parameters and simulations has been developed from data resampling and error modelling approaches. An original approach to integrating density-dependence in demographic processes has been developed, based on a density metric and the reintroduction of forest stand entities adapted to the model. A strategy for integrating climate forcing of model demographic parameters was developed based on an input-output coupling approach with the process-based model CASTANEA, for a subset of French forests including oak, beech, Norway spruce, and Scots pine forests. All of these developments significantly reduced the prediction bias of the initial model.Conclusions. These developments make MARGOT a much more reliable forest resource assessment tool, and are based on an original modeling approach that is unique in Europe. The use of ancient forest statistics will make it possible to evaluate the model and simulate the carbon stock of French forests over a longer time horizon (over 100 years). Intensive simulations to assess the performance of this new model must be done
Livros sobre o assunto "Uncertainty propagation in a dynamical context"
Louchet, Francois. Snow Avalanches. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198866930.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Uncertainty propagation in a dynamical context"
Chelle-Michou, Cyril, e Urs Schaltegger. "U–Pb Dating of Mineral Deposits: From Age Constraints to Ore-Forming Processes". In Isotopes in Economic Geology, Metallogenesis and Exploration, 37–87. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27897-6_3.
Texto completo da fonte"Propagation of Uncertainty in a Continuous Time Dynamical System". In Modeling and Inverse Problems in the Presence of Uncertainty, 223–322. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b16760-9.
Texto completo da fonteHans Alexander e Udluft Steffen. "Uncertainty Propagation for Efficient Exploration in Reinforcement Learning". In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-606-5-361.
Texto completo da fonteWest, Mike. "Some Statistical Issues in Palæoclimatology¹". In Bayesian Statistics 5, 461–84. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198523567.003.0024.
Texto completo da fonteChadwick, P., e N. H. Scott. "Linear dynamical stability in constrained thermoelasticity I. Deformation-temperature constraints". In Nonlinear Elasticity and Theoretical Mechanics, 125–34. Oxford University PressOxford, 1994. http://dx.doi.org/10.1093/oso/9780198534860.003.0011.
Texto completo da fonteChakraverty, S., e Smita Tapaswini. "Numerical Solution of Fuzzy Differential Equations and its Applications". In Advances in Computational Intelligence and Robotics, 127–49. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4991-0.ch007.
Texto completo da fonteBullough, R. K., e R. Hynne. "Ewald’s optical extinction theorem". In P. P. Ewald and his Dynamical Theory of X-ray Diffraction, 98–110. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198553793.003.0012.
Texto completo da fonteAntolin, William P., Aurélien Costes, Mélanie C. Rochoux e Patrick Le Moigne. "Accounting for the canopy drag effects on wildland fire spread in coupled atmosphere/fire simulations". In Advances in Forest Fire Research 2022, 959–64. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_145.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Uncertainty propagation in a dynamical context"
Zanoni, Andrea, Michele Zilletti, Gianni Cassoni, Carmen Talamo, Davide Marchesoli, Pierangelo Masarati e Francesca Colombo. "An Uncertainty Propagation Approach to Collective Bounce Rotorcraft-Pilot Couplings Analysis". In Vertical Flight Society 80th Annual Forum & Technology Display, 1–12. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1321.
Texto completo da fonteKumar, Alok, e Atul Kelkar. "Uncertainty Propagation in Dynamical Systems Using Koopman Eigenfunctions". In 2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE). IEEE, 2023. http://dx.doi.org/10.1109/cacre58689.2023.10209022.
Texto completo da fonteMezic, I. "Coupled nonlinear dynamical systems: asymptotic behavior and uncertainty propagation". In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1430303.
Texto completo da fonteTerejanu, Gabriel, Puneet Singla, Tarunraj Singh e Peter Scott. "Uncertainty Propagation for Nonlinear Dynamical Systems Using Gaussian Mixture Models". In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-7472.
Texto completo da fonteDiez, Matteo, Zhaoyuan Wang, Sungtek Park, Christian Milano, Frederick Stern, Hironori Yasukawa, Andrew Gunderson e John Scherer. "Multi-Fidelity MMG-Model for Digital Design of High-Speed Small Craft". In SNAME Power Boat Symposium. SNAME, 2024. http://dx.doi.org/10.5957/cpbs-2024-008.
Texto completo da fonteDe, Saibal, Reese Jones e Hemanth Kolla. "Uncertainty Propagation in Dynamical Systems via Stochastic Collocation on Model Dynamics." In Proposed for presentation at the USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP) held August 18-19, 2022 in Crystal City, Arlington, Virginia. US DOE, 2022. http://dx.doi.org/10.2172/2004300.
Texto completo da fonteDe, Saibal, Reese Jones e Hemanth Kolla. "Uncertainty Propagation in Dynamical Systems via Stochastic Collocation on Model Dynamics." In Proposed for presentation at the USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP) held August 18-19, 2022 in Crystal City, Arlington, Virginia. US DOE, 2022. http://dx.doi.org/10.2172/2004282.
Texto completo da fonteSchäfer, Felicitas, Shuai Guo e Wolfgang Polifke. "The Impact of Exceptional Points on the Reliability of Thermoacoustic Stability Analysis". In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15496.
Texto completo da fonteVarigonda, S., T. Kalmar-Nagy, B. LaBarre e I. Mezic. "Graph decomposition methods for uncertainty propagation in complex, nonlinear interconnected dynamical systems". In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1430306.
Texto completo da fonteRamapuram Matavalam, Amarsagar Reddy, Umesh Vaidya e Venkataramana Ajjarapu. "Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems". In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147295.
Texto completo da fonteRelatórios de organizações sobre o assunto "Uncertainty propagation in a dynamical context"
Banks, H. T., e Shuhua Hu. Propagation of Uncertainty in Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, outubro de 2011. http://dx.doi.org/10.21236/ada556937.
Texto completo da fonteParsons, Donald. A Tutorial for Generating Correlated Random Samples in the Context of Replica Cross Section Data Used in the Propagation of Uncertainty (Second Edition). Office of Scientific and Technical Information (OSTI), novembro de 2023. http://dx.doi.org/10.2172/2228641.
Texto completo da fonte