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Libros sobre el tema "Tensors methods"

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Publicado: 7 de julio de 2024

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1

P, Miles J., ed. Tensor methods for engineers. New York: Ellis Horwood, 1990.

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2

Tensor methods in statistics. London: Chapman and Hall, 1987.

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3

Borg, Sidney F. Matrix-tensor methods in continuum mechanics. 2a ed. Singapore: World Scientific, 1990.

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4

An introduction to tensors and group theory for physicists. New York: Birkhäuser, 2011.

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5

Montangero, Simone. Introduction to Tensor Network Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01409-4.

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6

Griffith, J. S. The irreducible tensor method for molecular symmetry groups. Mineola, NY: Dover Publications, 2006.

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7

H, Pulliam Thomas y Research Institute for Advanced Computer Science (U.S.), eds. Tensor-GMRES method for large sparse systems of nonlinear equations. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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8

Greenblatt, Seth A. Tensor methods for full-information maximum likelihood estimation: Unconstrained estimation. Reading: University of Reading. Department of Economics, 1992.

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9

1968-, VanPool Todd L. y VanPool Christine S. 1969-, eds. Essential tensions in archaeological method and theory. Salt Lake City: University of Utah Press, 2003.

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10

Giraldo, Francis X. An Introduction to Element-Based Galerkin Methods on Tensor-Product Bases. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55069-1.

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11

Ran, Shi-Ju. Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems. Cham: Springer Nature, 2020.

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12

Greenblatt, Seth A. Tensor methods for full-information maximum likelihood estimation: Estimation with parameter constraints. Reading: University of Reading. Department of Economics, 1992.

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13

Maslia, Morris L. Methods and computer program documentation for determining anisotropic transmissivity tensor components of two-dimensional ground-water flow. [Washington]: U.S. G.P.O., 1987.

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14

Kondrat'ev, Gennadiy. Clifford Geometric Algebra. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1832489.

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The monograph is devoted to the fundamental aspects of geometric algebra and closely related issues. The category of Clifford algebras is considered as the conjugate category of vector spaces with a quadratic form. Possible constructions in this category and internal algebraic operations of an algebra with a geometric interpretation are studied. An application to the differential geometry of a Euclidean manifold based on a shape tensor is included. We consider products, coproducts and tensor products in the category of associative algebras with application to the decomposition of Clifford algebras into simple components. Spinors are introduced. Methods of matrix representation of the Clifford algebra are studied. It may be of interest to students, postgraduates and specialists in the field of mathematics, physics and cybernetics.
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15

M, Sindir Munir y United States. National Aeronautics and Space Administration., eds. Comparative study of advanced turbulence models for turbomachinery: Contract NAS8-38860, final report. [Washington, DC: National Aeronautics and Space Administration, 1996.

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16

Fedorov, Y. N. A memoir on integrable systems. New York: Springer, 2003.

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17

Fröhlich, Jürg. Quantum groups, quantum categories, and quantum field theory. Berlin: Springer-Verlag, 1993.

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18

McCullagh, Peter. Tensor Methods in Statistics. Dover Publications, Incorporated, 2018.

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19

Khoromskij, Boris N. Tensor Numerical Methods in Scientific Computing. de Gruyter GmbH, Walter, 2018.

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20

Khoromskaia, Venera y Boris N. Khoromskij. Tensor Numerical Methods in Quantum Chemistry. de Gruyter GmbH, Walter, 2018.

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21

Khoromskij, Boris N. Tensor Numerical Methods in Scientific Computing. de Gruyter GmbH, Walter, 2018.

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22

Khoromskij, Boris N. Tensor Numerical Methods in Scientific Computing. de Gruyter GmbH, Walter, 2018.

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23

Khoromskaia, Venera y Boris N. Khoromskij. Tensor Numerical Methods in Quantum Chemistry. de Gruyter GmbH, Walter, 2018.

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24

Liu, Yipeng. Tensors for Data Processing: Theory, Methods and Applications. Academic Press, 2021.

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25

Liu, Yipeng. Tensors for Data Processing: Theory, Methods and Applications. Elsevier Science & Technology Books, 2021.

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26

McCullagh, Peter. Tensor Methods in Statistics: Second Edition. Dover Publications, Incorporated, 2018.

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27

Oseledets, Ivan. Numerical Tensor Methods: Tensor Trains in Mathematics and Computer Science. de Gruyter GmbH, Walter, 2025.

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28

Oseledets, Ivan. Numerical Tensor Methods: Tensor Trains in Mathematics and Computer Science. de Gruyter GmbH, Walter, 2025.

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29

Oseledets, Ivan. Numerical Tensor Methods: Tensor Trains in Mathematics and Computer Science. de Gruyter GmbH, Walter, 2025.

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30

Khoromskaia, Venera y Boris Khoromskij. Tensor Numerical Methods in Electronic Structure Calculations: Basic Algorithms and Applications. De Gruyter, Inc., 2018.

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31

From Vectors to Tensors. Springer London, Limited, 2006.

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32

Silver, Brian L. y Ernest M. Loebl. Irreducible Tensor Methods: An Introduction for Chemists. Elsevier Science & Technology Books, 2013.

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33

Tensorial Methods and Renormalization in Group Field Theories. Springer, 2014.

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34

McCullagh, P. Tensor Methods in Statistics: Monographs on Statistics and Applied Probability. Taylor & Francis Group, 2018.

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35

McCullagh, P. Tensor Methods in Statistics: Monographs on Statistics and Applied Probability. Taylor & Francis Group, 2018.

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36

McCullagh, P. Tensor Methods in Statistics: Monographs on Statistics and Applied Probability. Taylor & Francis Group, 2018.

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37

McCullagh, P. Tensor Methods in Statistics: Monographs on Statistics and Applied Probability. Taylor & Francis Group, 2018.

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38

Tensor Methods in Statistics: Monographs on Statistics and Applied Probability. Taylor & Francis Group, 2017.

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39

Jeevanjee, Nadir. An Introduction to Tensors and Group Theory for Physicists. Birkhäuser, 2015.

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40

Kardestuncer, Hayrettin. Finite Elements Methods Via Tensors: Course Held at the Department of Mechanics of Solids, June 1972. Springer London, Limited, 2014.

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41

Carrozza, Sylvain. Tensorial Methods and Renormalization in Group Field Theories. Springer, 2016.

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42

Carrozza, Sylvain. Tensorial Methods and Renormalization in Group Field Theories. Springer London, Limited, 2014.

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43

(Editor), Joachim Weickert y Hans Hagen (Editor), eds. Visualization and Processing of Tensor Fields (Mathematics and Visualization). Springer, 2005.

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44

Penrose, Roger y Wolfgang Rindler. Spinors and Space-Time: Volume 2, Spinor and Twistor Methods in Space-Time Geometry. Cambridge University Press, 2009.

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45

McCullagh, Peter. Tensor Methods in Statistics. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781351077118.

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46

Griffith, J. S. Irreducible Tensor Method for Molecular Symmetry Groups. Dover Publications, Incorporated, 2013.

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47

Geophysics today: A survey of the field as the journal celebrates its 75th anniversary. Tulsa, OK: Society of Exploration Geophysicists, 2010.

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48

Wilde, Elisabeth A., Kareem W. Ayoub y Asim F. Choudhri. Diffusion Tensor Imaging. Editado por Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.10.

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Diffusion tensor imaging (DTI) is a method of specifying and visualizing the functional integrity of white matter tracts that contribute to the functional and structural connectivity among different brain regions through the examination of water diffusion through tissue. It has gained rapid popularity in the past two decades, particularly for elucidating the process of normal white matter development and the effects of aging on it, as well as providing some insights into the possible neuroanatomical correlates of numerous psychiatric and neurologic disorders. This chapter outlines the instrumentation and the procedures employed in deriving estimates of the functional integrity of anatomical connections in the brain, and issues regarding the reliability and validity of the different DTI procedures are systematically addressed.
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49

Tensor-GMRES method for large sparse systems of nonlinear equations. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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50

Tensor-GMRES method for large sparse systems of nonlinear equations. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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