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1

Friswell, M. I. Finite element model updating in structural dynamics. Kluwer Academic Publishers, 1995.

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2

Craig, Roy R. Substructure system identification for finite element model updating: Final report, NASA grant no. NAG8-1130. Center for Aeronautical Research, Bureau of Engineering Research, the University of Texas at Austin, 1997.

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3

Groshev, Igor', and Andrey Zheregelya. Managing an organization's digital transformation in the context of the emerging data economy. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2150883.

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The monograph is devoted to the problem of managing the digital transformation of an organization in the context of the formation of the digital ecosystem of the modern economy. The digital transformation of socio-economic systems, the business sphere, and social life has become dominant in Russian society in recent years, accelerating commercial and business interaction, accelerating the economy, and forming new communication flows. It is based on the methodology of an integrated approach and system analysis. The theoretical basis is the publications of domestic and foreign scientists and pra
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4

Steyerberg, Ewout W. Clinical prediction models: A practical approach to development, validation, and updating. Springer, 2009.

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5

Marwala, Tshilidzi. Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics. Springer, 2010.

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6

Marwala, Tshilidzi. Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics. Springer, 2014.

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7

Marwala, Tshilidzi, Sondipon Adhikari, and Ilyes Boulkaibet. Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering. Wiley & Sons, Limited, John, 2016.

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8

Marwala, Tshilidzi, Sondipon Adhikari, and Ilyes Boulkaibet. Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering. Wiley & Sons, Limited, John, 2016.

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9

Marwala, Tshilidzi, Sondipon Adhikari, and Ilyes Boulkaibet. Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering. Wiley & Sons, Incorporated, John, 2016.

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10

Marwala, Tshilidzi, Sondipon Adhikari, and Ilyes Boulkaibet. Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering. Wiley & Sons, Incorporated, John, 2016.

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11

Mottershead, John, Mike Friswell, and Michael Link. Sensitivity Methods in Model Updating. Elsevier Science, 2008.

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12

FiniteElementModel Updating Using Computational Intelligence Techniques. Springer, 2010.

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13

Boudreau, Joseph F., and Eric S. Swanson. Quantum field theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0024.

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Markov chain Monte Carlo techniques are developed to compute properties of a variety of quantum field theories. The method is introduced with a simple scalar field theory and used to evaluate the particle spectrum and phase diagram for parity symmetry breaking. The technique of micorcanonical updating is introduced to increase efficiency. The important topic of gauge theory is then introduced via the gauged Z2 model. Development of the gauge theory formalism continues with Abelian gauge theory in two dimensions. The interaction between static charges is computed and compared to the exact resul
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14

Steyerberg, Ewout W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer, 2020.

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15

Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer, 2010.

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Clinical prediction models: A practical approach to development, validation, and updating. Springer, 2009.

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17

Steyerberg, Ewout W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer, 2019.

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