Books on the topic 'Boundary estimation'

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1

Banks, H. Thomas. Boundary estimation problems arising in thermal tomography. Hampton, Va: Institute for Computer Applications in Science and Engineering, 1989.

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2

Banks, H. Thomas. Boundary estimation problems arising in thermal tomography. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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3

Sunahara, Y. A method of boundary parameter estimation for a two-dimensional diffusion system under noisy observations. Hampton, Va: ICASE, 1987.

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4

Banks, H. Thomas. Boundary shape identification problems in two-dimensional domains related to thermal testing of materials. Hampton, Va: ICASE, 1988.

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5

Vàzquez, Rafael. Control of turbulent and magnetohydrodynamic channel flows: Boundary stabilization and state estimation. Boston: Birkhäuser, 2008.

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6

Vàzquez, Rafael. Control of turbulent and magnetohydrodynamic channel flows: Boundary stabilization and state estimation. Boston: Birkhäuser, 2008.

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7

Kojima, Fumio. Parameter estimation for boundary value problems by integral equations of the second kind. Hampton, Va: ICASE, 1988.

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8

McBride, Marvin B. Estimation of stratocumulus-topped boundary layer depth using sea surface and remotely sensed cloud-top temperatures. Monterey, Calif: Naval Postgraduate School, 2000.

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9

Gunson, James R. Estimating open-ocean boundary conditions: Sensitivity studies. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1995.

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10

Gunson, James R. Estimating open-ocean boundary conditions: Sensitivity studies. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1995.

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11

Barnett, Alex, 1972 December 7- editor of compilation, ed. Spectral geometry. Providence, Rhode Islands: American Mathematical Society, 2012.

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12

A, Murphy Katherine, and United States. National Aeronautics and Space Administration, eds. Estimation of discontinuous coefficients and boundary parameters for hyperbolic systems. [Washington, D.C: National Aeronautics and Space Administration, 1986.

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13

Control of Turbulent and Magnetohydrodynamic Channel Flows: Boundary Stabilization and State Estimation. Springer London, Limited, 2008.

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14

Control of Turbulent and Magnetohydrodynamic Channel Flows: Boundary Stabilization and State Estimation (Systems & Control: Foundations & Applications). Birkhäuser Boston, 2007.

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15

National Aeronautics and Space Administration (NASA) Staff. Parameter Estimation for Boundary Value Problems by Integral Equations of the Second Kind. Independently Published, 2018.

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16

Kren, Richard J. Estimation of marine boundary layer depth and relative humidity with multispectral satellite measurements. 1987.

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17

United States. Federal Aviation Administration. and Langley Research Center, eds. A simple, analytical, axisymmetric microburst model for downdraft estimation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1991.

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18

Estimation of Stratocumulus-Topped Boundary Layer Depth Using Sea Surface and Remotely Sensed Cloud-Top Temperatures. Storming Media, 2000.

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19

Jongil, Han, and Langley Research Center, eds. An estimation of turbulent kinetic energy and energy dissipation rate based on atomspheric boundary layer similarity theory. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 2000.

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20

Inoue, Shawna K. A moving boundary model of calcium alginate gel formation and the estimation of diffusion and mass transfer coefficients. 1997.

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21

Cheng, Russell. The Pearson and Johnson Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0009.

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This chapter re-examines two of the best-known systems of parametric distributions: the Pearson and the Johnson. It is shown that, in the Pearson system, Pearson Types III and V are boundary embedded models of the main Types I, IV, and VI. A comprehensive way of finding the best type to fit is given using appropriate score statistics to guide a systematic search of all model types, including symmetric boundary models. Maximum likelihood estimation is used and details of its numerical implementation are given. Type IV can be a difficult model to fit. A method is discussed for this model that is reasonably robust, subject to certain restrictions on the parameter values. The same examination is made of the Johnson system where the lognormal, SL family is shown to be an embedded subsystem of both the main subsystems SB and SU. Two real data examples are given.
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22

Mei, Hsü, Eberhard Wynn, and United States. National Aeronautics and Space Administration., eds. Estimations of ABL fluxes and other turbulence parameters from Doppler Lidar Data. [Washington, DC: National Aeronautics and Space Administration, 1989.

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23

Boudreau, Joseph F., and Eric S. Swanson. Continuum dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0019.

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The theory and application of a variety of methods to solve partial differential equations are introduced in this chapter. These methods rely on representing continuous quantities with discrete approximations. The resulting finite difference equations are solved using algorithms that stress different traits, such as stability or accuracy. The Crank-Nicolson method is described and extended to multidimensional partial differential equations via the technique of operator splitting. An application to the time-dependent Schrödinger equation, via scattering from a barrier, follows. Methods for solving boundary value problems are explored next. One of these is the ubiquitous fast Fourier transform which permits the accurate solution of problems with simple boundary conditions. Lastly, the finite element method that is central to modern engineering is developed. Methods for generating finite element meshes and estimating errors are also discussed.
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24

Estimating the Height of the Planetary Boundary Layer for Diffusion- Transport Models: A Four Algorithm Comparison. Storming Media, 1999.

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25

Cheng, Russell. Non-Standard Problems: Some Examples. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0002.

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This chapter provides motivation for the rest of the book by giving a selection of simple examples showing how non-standard behaviour can occur. The well-known maximum likelihood estimator is used throughout this book to estimate an unknown vector of parameters. Its behaviour is standard if the log-likelihood is a concave quadratic function with the maximum in the neighbourhood of the true parameter value, but is otherwise non-standard. Examples of non-standard situations given in this chapter include the true parameter value not being an internal point of the parameter space, but being on a fixed boundary that may not even be finite, or where the mathematical form of the log-likelihood is different with non-estimable indeterminate parameters, or where the true model is an embedded model. Other examples given include where the log-likelihood is unbounded at a finite parameter point, is discontinuous, or is no longer quadratic.
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