Bibliografias temáticas / Karhunen-Loève theorem

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Literatura científica selecionada sobre o tema "Karhunen-Loève theorem"

Autor: Grafiati
Publicado: 4 de junho de 2021
Última modificação: 6 de fevereiro de 2022

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Artigos de revistas sobre o assunto "Karhunen-Loève theorem"

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Bay, Xavier, and Jean-Charles Croix. "Karhunen–Loève decomposition of Gaussian measures on Banach spaces." Probability and Mathematical Statistics 39, no. 2 (December 19, 2019): 279–97. http://dx.doi.org/10.19195/0208-4147.39.2.3.

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The study of Gaussian measures on Banach spaces is of active interest both in pure and applied mathematics. In particular, the spectral theorem for self-adjoint compact operators on Hilbert spaces provides a canonical decomposition of Gaussian measures on Hilbert spaces, the socalled Karhunen–Ločve expansion. In this paper, we extend this result to Gaussian measures on Banach spaces in a very similar and constructive manner. In some sense, this can also be seen as a generalization of the spectral theorem for covariance operators associated with Gaussian measures on Banach spaces. In the specia
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Leite, F. E. A., Raúl Montagne, G. Corso, and L. S. Lucena. "Karhunen–Loève spectral analysis in multiresolution decomposition." Computational Geosciences 13, no. 2 (June 27, 2008): 165–70. http://dx.doi.org/10.1007/s10596-008-9091-0.

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Khoromskij, B. N., A. Litvinenko, and H. G. Matthies. "Application of hierarchical matrices for computing the Karhunen–Loève expansion." Computing 84, no. 1-2 (October 31, 2008): 49–67. http://dx.doi.org/10.1007/s00607-008-0018-3.

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Glösmann, Philipp. "Reduction of discrete element models by Karhunen–Loève transform: a hybrid model approach." Computational Mechanics 45, no. 4 (December 19, 2009): 375–85. http://dx.doi.org/10.1007/s00466-009-0456-6.

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Nakamura, Yukihiro, Hidekazu Kaneko, Tohru Kiryu, Shinya S. Suzuki, and Yoshiaki Saitoh. "Influence of motor unit firing patterns on evaluation of muscle activities by Karhunen-Loève expansion." Systems and Computers in Japan 34, no. 12 (September 11, 2003): 45–55. http://dx.doi.org/10.1002/scj.10326.

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Rauter, Natalie. "A computational modeling approach based on random fields for short fiber-reinforced composites with experimental verification by nanoindentation and tensile tests." Computational Mechanics 67, no. 2 (January 18, 2021): 699–722. http://dx.doi.org/10.1007/s00466-020-01958-3.

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AbstractIn this study a modeling approach for short fiber-reinforced composites is presented which allows one to consider information from the microstructure of the compound while modeling on the component level. The proposed technique is based on the determination of correlation functions by the moving window method. Using these correlation functions random fields are generated by the Karhunen–Loève expansion. Linear elastic numerical simulations are conducted on the mesoscale and component level based on the probabilistic characteristics of the microstructure derived from a two-dimensional m
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Blondeel, Philippe, Pieterjan Robbe, Cédric Van hoorickx, Stijn François, Geert Lombaert, and Stefan Vandewalle. "p-Refined Multilevel Quasi-Monte Carlo for Galerkin Finite Element Methods with Applications in Civil Engineering." Algorithms 13, no. 5 (April 28, 2020): 110. http://dx.doi.org/10.3390/a13050110.

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Civil engineering applications are often characterized by a large uncertainty on the material parameters. Discretization of the underlying equations is typically done by means of the Galerkin Finite Element method. The uncertain material parameter can be expressed as a random field represented by, for example, a Karhunen–Loève expansion. Computation of the stochastic responses, i.e., the expected value and variance of a chosen quantity of interest, remains very costly, even when state-of-the-art Multilevel Monte Carlo (MLMC) is used. A significant cost reduction can be achieved by using a rece
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Jankoski, Radoslav, Ulrich Römer, and Sebastian Schöps. "Modeling of spatial uncertainties in the magnetic reluctivity." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 4 (July 3, 2017): 1151–67. http://dx.doi.org/10.1108/compel-10-2016-0438.

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Purpose The purpose of this paper is to present a computationally efficient approach for the stochastic modeling of an inhomogeneous reluctivity of magnetic materials. These materials can be part of electrical machines such as a single-phase transformer (a benchmark example that is considered in this paper). The approach is based on the Karhunen–Loève expansion (KLE). The stochastic model is further used to study the statistics of the self-inductance of the primary coil as a quantity of interest (QoI). Design/methodology/approach The computation of the KLE requires solving a generalized eigenv
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Teses / dissertações sobre o assunto "Karhunen-Loève theorem"

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Giambartolomei, Giordano. "The Karhunen-Loève theorem." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10169/.

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La trasformata di Karhunen-Loève monodimensionale è la decomposizione di un processo stocastico del secondo ordine a parametrizzazione continua in coefficienti aleatori scorrelati. Nella presente dissertazione, la trasformata è ottenuta per via analitica, proiettando il processo, considerato in un intervallo di tempo limitato [a,b], su una base deterministica ottenuta dalle autofunzioni dell'operatore di Hilbert-Schmidt di covarianza corrispondenti ad autovalori positivi. Fondamentalmente l'idea del metodo è, dal primo, trovare gli autovalori positivi dell'operatore integrale di Hilbert-Schmid
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Ling, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.

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Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems’ natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in
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Trabalhos de conferências sobre o assunto "Karhunen-Loève theorem"

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Stanescu, Daniela, Lucian Ionel Gaina, Bianca Gusita, and Ioana Ghergulescu. "Message Processing-based Steganographic Algorithm using Karhunen-Loève Transform." In 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2019. http://dx.doi.org/10.1109/icstcc.2019.8886015.

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