Literatura académica sobre el tema "Polynomial chao"
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Artículos de revistas sobre el tema "Polynomial chao"
Schmid, Christine y Kyle J. DeMars. "Angular Correlation Using Rogers-Szegő-Chaos". Mathematics 8, n.º 2 (1 de febrero de 2020): 171. http://dx.doi.org/10.3390/math8020171.
Texto completoChen, Hong, Ling Wu, Shu Bin Gu y Qun Ding. "Comparison of the Image Encryption Effects Based on Different Unary Polynomial Transformation Chaos". Advanced Materials Research 846-847 (noviembre de 2013): 948–51. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.948.
Texto completoGhanem, R. "The Nonlinear Gaussian Spectrum of Log-Normal Stochastic Processes and Variables". Journal of Applied Mechanics 66, n.º 4 (1 de diciembre de 1999): 964–73. http://dx.doi.org/10.1115/1.2791806.
Texto completoFan, Chengmei, M. Mobeen Munir, Zafar Hussain, Muhammad Athar y Jia-Bao Liu. "Polynomials and General Degree-Based Topological Indices of Generalized Sierpinski Networks". Complexity 2021 (9 de febrero de 2021): 1–10. http://dx.doi.org/10.1155/2021/6657298.
Texto completoSEPAHVAND, K., S. MARBURG y H. J. HARDTKE. "UNCERTAINTY QUANTIFICATION IN STOCHASTIC SYSTEMS USING POLYNOMIAL CHAOS EXPANSION". International Journal of Applied Mechanics 02, n.º 02 (junio de 2010): 305–53. http://dx.doi.org/10.1142/s1758825110000524.
Texto completoXiu, Dongbin, Didier Lucor, C. H. Su y George Em Karniadakis. "Stochastic Modeling of Flow-Structure Interactions Using Generalized Polynomial Chaos". Journal of Fluids Engineering 124, n.º 1 (29 de octubre de 2001): 51–59. http://dx.doi.org/10.1115/1.1436089.
Texto completoGao, Rugao, Keping Zhou y Yun Lin. "A Flexible Polynomial Expansion Method for Response Analysis with Random Parameters". Complexity 2018 (3 de diciembre de 2018): 1–14. http://dx.doi.org/10.1155/2018/7471460.
Texto completoFranco-Medrano, Fermin y Francisco J. Solis. "Stability of Real Parametric Polynomial Discrete Dynamical Systems". Discrete Dynamics in Nature and Society 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/680970.
Texto completoAbbasi, Mostafa y Ali Gholami. "Polynomial chaos expansion for nonlinear geophysical inverse problems". GEOPHYSICS 82, n.º 4 (1 de julio de 2017): R259—R268. http://dx.doi.org/10.1190/geo2016-0716.1.
Texto completoLi, Ning, Bo Meng, Xinlong Feng y Dongwei Gui. "A Numerical Comparison of Finite Difference and Finite Element Methods for a Stochastic Differential Equation with Polynomial Chaos". East Asian Journal on Applied Mathematics 5, n.º 2 (mayo de 2015): 192–208. http://dx.doi.org/10.4208/eajam.250714.020515a.
Texto completoTesis sobre el tema "Polynomial chao"
Xiaochen, Liu. "Statistical Analysis of Integrated Circuits Using Decoupled Polynomial Chaos". Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34836.
Texto completoYorke, Rory. "Chaos control using local polynomial approximation". Master's thesis, University of Cape Town, 2001. http://hdl.handle.net/11427/5075.
Texto completoChaotic systems may be defined as those whose behaviour is sensitively dependent on initial conditions. Such systems may be made periodic using small input perturbations, as proposed in [OGY90]; this is called Ott-Grebogi-Yorke (OGY) chaos control. The original method used a linear model for controller design; a later development of chaos control was [CCdF99], in which a polynomial model is used. This dissertation proposes using local Taylor polynomial models as a basis for chaos control.
Templeton, Brian Andrew. "A Polynomial Chaos Approach to Control Design". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28840.
Texto completoPh. D.
Whittle, Lisa. "Stochastic Optimal Trajectory Generation via Multivariate Polynomial Chaos". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65746.
Texto completoSzepietowska, Katarzyna. "POLYNOMIAL CHAOS EXPANSION IN BIO- AND STRUCTURAL MECHANICS". Thesis, Bourges, INSA Centre Val de Loire, 2018. http://www.theses.fr/2018ISAB0004/document.
Texto completoThis thesis presents a probabilistic approach to modelling the mechanics of materials and structures where the modelled performance is influenced by uncertainty in the input parameters. The work is interdisciplinary and the methods described are applied to medical and civil engineering problems. The motivation for this work was the necessity of mechanics-based approaches in the modelling and simulation of implants used in the repair of ventral hernias. Many uncertainties appear in the modelling of the implant-abdominal wall system. The probabilistic approach proposed in this thesis enables these uncertainties to be propagated to the output of the model and the investigation of their respective influences. The regression-based polynomial chaos expansion method is used here. However, the accuracy of such non-intrusive methods depends on the number and location of sampling points. Finding a universal method to achieve a good balance between accuracy and computational cost is still an open question so different approaches are investigated in this thesis in order to choose an efficient method. Global sensitivity analysis is used to investigate the respective influences of input uncertainties on the variation of the outputs of different models. The uncertainties are propagated to the implant-abdominal wall models in order to draw some conclusions important for further research. Using the expertise acquired from biomechanical models, modelling of historic timber joints and simulations of their mechanical behaviour is undertaken. Such an investigation is important owing to the need for efficient planning of repairs and renovation of buildings of historical value
Nydestedt, Robin. "Application of Polynomial Chaos Expansion for Climate Economy Assessment". Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223985.
Texto completoInom klimatekonomi används integrated assessment models (IAMs) för att förutspå hur klimatförändringar påverkar ekonomin. Dessa IAMs modellerar komplexa interaktioner mellan geofysiska och mänskliga system för att kunna kvantifiera till exempel kostnaden för den ökade koldioxidhalten på planeten, i.e. Social Cost of Carbon (SCC). Detta representerar den ekonomiska kostnaden som motsvaras av utsläppet av ett ton koldioxid. Faktumet att både de geofysiska och ekonomiska submodulerna är stokastiska gör att SCC-uppskattningar varierar mycket även inom väletablerade IAMs som PAGE och DICE. Variationen grundar sig i skillnader inom modellerna men också från att val av sannolikhetsfördelningar för de stokastiska variablerna skiljer sig. Eftersom IAMs ofta är formulerade som optimeringsproblem leder dessutom osäkerheterna till höga beräkningskostnader. I denna uppsats introduceras en ny IAM, FAIR/DICE, som är en diskret tids hybrid av DICE och FAIR. Den utgör en potentiell förbättring av DICE eftersom klimat- och kolmodulerna i FAIR även behandlar återkoppling från klimatmodulen till kolmodulen. FAIR/DICE är analyserad med hjälp av Polynomial Chaos Expansions (PCEs), ett alternativ till Monte Carlo-metoder. Med hjälp av PCEs kan de osäkerheter projiceras på stokastiska polynomrum vilket har fördelen att beräkningskostnader reduceras men nackdelen att lagringskraven ökar. Detta eftersom många av beräkningarna kan sparas från första simuleringen av systemet, dessutom kan statistik extraheras direkt från PCE koefficienterna utan behov av sampling. FAIR/DICE systemet projiceras med hjälp av PCEs där en osäkerhet är introducerad via equilibrium climate sensitivity (ECS), vilket i sig är ett värde på hur känsligt klimatet är för koldioxidförändringar. ECS modelleras med hjälp av en fyra-parameters Beta sannolikhetsfördelning. Avslutningsvis jämförs resultat i medelvärde och varians mellan PCE implementationen av FAIR/DICE och en Monte Carlo-baserad referens, därefter ges förslag på framtida utvecklingsområden.
Perez, Rafael A. "Uncertainty Analysis of Computational Fluid Dynamics Via Polynomial Chaos". Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28984.
Texto completoPh. D.
Ishak, Hassoun. "Étude stochastique de l'impact des défauts de porosités et de plissements dans les matériaux composites". Thesis, Nantes, 2017. http://www.theses.fr/2017NANT4090/document.
Texto completoComposite materials are increasingly used in various fields such as aerospace or renewable marine energies due to their excellent specific properties. However, the manufacturing processes of the composite structures are complex, which can lead to the appearance of defects, particularly wrinkles and porosities, which affect the mechanical properties of the structure. Based on conservative criteria, a system of non-destructive testing of composite parts thus makes it possible to judge their conformity. In case of non-conformity, those components are rejected, with non-negligible economic consequences. The objective of this study is to quantify the impact of the defects and associated uncertainties on the behavior of composite parts. In this work, we adopt a parametric vision of the uncertainties consisting in representing the probabilistic content through a finite set of random variables. We focus on the propagation of uncertainties based on spectral stochastic methods. The study involving porosity is done at the micro-scale and then at the macro-scale. The random input parameters are related to the geometry of the porosities and their rates. The study of the wrinkle defect, done at the mesoscopic scale, is based on a parametric representation of the geometry of the wrinkle. The random input parameters then represent the shape and size of this defect. It is therefore possible to analyze the impact of these two manufacturing defects at a structural scale through classical mechanical quantities and check the failure of the structure with failure criteria
Koehring, Andrew. "The application of polynomial response surface and polynomial chaos expansion metamodels within an augmented reality conceptual design environment". [Ames, Iowa : Iowa State University], 2008.
Buscar texto completoFisher, James Robert. "Stability analysis and control of stochastic dynamic systems using polynomial chaos". [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2853.
Texto completoLibros sobre el tema "Polynomial chao"
Pettersson, Mass Per, Gianluca Iaccarino y Jan Nordström. Polynomial Chaos Methods for Hyperbolic Partial Differential Equations. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10714-1.
Texto completoOzen, Hasan Cagan. Long Time Propagation of Stochasticity by Dynamical Polynomial Chaos Expansions. [New York, N.Y.?]: [publisher not identified], 2017.
Buscar texto completoS, Taqqu Murad, ed. Wiener chaos: Moments, cumulants and diagrams : a survey with computer implementation. Milan: Springer, 2011.
Buscar texto completoWiener Chaos : Moments, Cumulants and Diagrams: A Survey with Computer Implementation. Springer Milan, 2011.
Buscar texto completoNordström, Jan, Mass Per Pettersson y Gianluca Iaccarino. Polynomial Chaos Methods for Hyperbolic Partial Differential Equations: Numerical Techniques for Fluid Dynamics Problems in the Presence of Uncertainties. Springer, 2015.
Buscar texto completoNordström, Jan, Mass Per Pettersson y Gianluca Iaccarino. Polynomial Chaos Methods for Hyperbolic Partial Differential Equations: Numerical Techniques for Fluid Dynamics Problems in the Presence of Uncertainties. Springer, 2016.
Buscar texto completoNordström, Jan, Mass Per Pettersson y Gianluca Iaccarino. Polynomial Chaos Methods for Hyperbolic Partial Differential Equations: Numerical Techniques for Fluid Dynamics Problems in the Presence of Uncertainties. Springer, 2015.
Buscar texto completoPeccati, Giovanni y Murad S. Taqqu. Wiener Chaos : Moments, Cumulants and Diagrams: A survey with Computer Implementation. Springer, 2014.
Buscar texto completoCapítulos de libros sobre el tema "Polynomial chao"
Pettersson, Mass Per, Gianluca Iaccarino y Jan Nordström. "Polynomial Chaos Methods". En Polynomial Chaos Methods for Hyperbolic Partial Differential Equations, 23–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10714-1_3.
Texto completoHesthaven, Jan S. y Dongbin Xiu. "Polynomial Chaos Expansions". En Encyclopedia of Applied and Computational Mathematics, 1162–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_331.
Texto completoChu, Liu. "Polynomial Chaos Expansion". En Uncertainty Quantification of Stochastic Defects in Materials, 37–49. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003226628-5.
Texto completoLototsky, Sergey V. y Boris L. Rozovsky. "The Polynomial Chaos Method". En Universitext, 233–380. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58647-2_5.
Texto completoDmitrishin, Dmitriy, Anna Khamitova y Alexander M. Stokolos. "Fejér Polynomials and Chaos". En Springer Proceedings in Mathematics & Statistics, 49–75. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10545-1_7.
Texto completoGhanem, Roger y John Red-Horse. "Polynomial Chaos: Modeling, Estimation, and Approximation". En Handbook of Uncertainty Quantification, 521–51. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_13.
Texto completoGhanem, Roger y John Red-Horse. "Polynomial Chaos: Modeling, Estimation, and Approximation". En Handbook of Uncertainty Quantification, 1–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-11259-6_13-1.
Texto completoXiu, Dongbin, Didier Lucor, C. H. Su y George Em Karniadakis. "Performance Evaluation of Generalized Polynomial Chaos". En Lecture Notes in Computer Science, 346–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44864-0_36.
Texto completoRusso, Francesco y Pierre Vallois. "Hermite Polynomials and Wiener Chaos Expansion". En Stochastic Calculus via Regularizations, 309–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_9.
Texto completoPeccati, Giovanni y Murad S. Taqqu. "Some facts about Charlier polynomials". En Wiener Chaos: Moments, Cumulants and Diagrams, 171–75. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1679-8_10.
Texto completoActas de conferencias sobre el tema "Polynomial chao"
Gerritsma, Marc, Peter Vos y Jan‐Bart van der Steen. "Time‐Dependent Polynomial Chaos". En NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2990897.
Texto completoXu, Can, Zhao Liu, Wei Tao y Ping Zhu. "A Novel Hierarchical Framework for Uncertainty Analysis of Multiscale Systems Combined Vine Copula With Sparse PCE". En ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97832.
Texto completoMai, Chu V. y Bruno Sudret. "HIERARCHICAL ADAPTIVE POLYNOMIAL CHAOS EXPANSIONS". En 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2015. http://dx.doi.org/10.7712/120215.4253.517.
Texto completoBakhtiari-Nejad, Firooz, Naserodin Sepehry y Mahnaz Shamshirsaz. "Polynomial Chaos Expansion Sensitivity Analysis for Electromechanical Impedance of Plate". En ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59129.
Texto completoTagade, Piyush M. y Han-Lim Choi. "A Polynomial Chaos Based Bayesian Inference Method With Uncertain Hyper-Parameters". En ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47632.
Texto completoThapa, Mishal, Sameer B. Mulani y Robert W. Walters. "Polynomial Chaos Decomposition with Differentiation Operation". En 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4288.
Texto completoSmith, A., A. Monti y F. Ponci. "Robust Controller Using Polynomial Chaos Theory". En Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting. IEEE, 2006. http://dx.doi.org/10.1109/ias.2006.256892.
Texto completoCheng, Haiyan y Adrian Sandu. "Collocation least-squares polynomial chaos method". En the 2010 Spring Simulation Multiconference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878621.
Texto completoMulani, Sameer, Rakesh Kapania y Robert Walters. "Stochastic Eigenvalue Problem with Polynomial Chaos". En 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-2068.
Walters, Robert. "Stochastic Fluid Mechanics via Polynomial Chaos". En 41st Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-413.
Texto completoInformes sobre el tema "Polynomial chao"
Nance, Douglas V. Stochastic Estimation via Polynomial Chaos. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2015. http://dx.doi.org/10.21236/ada627811.
Texto completoJakeman, John, Fabian Franzelin, Akil Narayan, Michael Eldred y Dirk Pflueger. Polynomial chaos expansions for dependent random variables. Office of Scientific and Technical Information (OSTI), mayo de 2019. http://dx.doi.org/10.2172/1762354.
Texto completoJardak, M., C. Su y G. E. Karniadakis. Spectral Polynomial Chaos Solutions of the Stochastic Advection Equation. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2001. http://dx.doi.org/10.21236/ada460601.
Texto completoXiu, Dongbin y George E. Karniadakis. The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, enero de 2003. http://dx.doi.org/10.21236/ada460654.
Texto completoXiu, Dong y George E. Karniadakis. Modeling Uncertainty in Flow Simulations via Generalized Polynomial Chaos. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2002. http://dx.doi.org/10.21236/ada461813.
Texto completoField, Jr, Richard V., .) y Mircea Grigoriu. Convergence properties of polynomial chaos approximations for L2 random variables. Office of Scientific and Technical Information (OSTI), marzo de 2007. http://dx.doi.org/10.2172/903430.
Texto completoXiu, Dongbin, Didier Lucor, C. Su y George E. Karniadakis. Stochastic Modeling of Flow-Structure Interactions using Generalized Polynomial Chaos. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2001. http://dx.doi.org/10.21236/ada461832.
Texto completoXiu, Dongbin y George E. Karniadakis. Modeling Uncertainty in Steady State Diffusion Problems via Generalized Polynomial Chaos. Fort Belvoir, VA: Defense Technical Information Center, julio de 2002. http://dx.doi.org/10.21236/ada460658.
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