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Книги з теми "Physical Predictions"

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Physical Predictions.
Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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1

Dietrich, Daniel S. Predicting radiation characteristics from antenna physical dimensions. Monterey, Calif: Naval Postgraduate School, 1992.

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2

Gerry, Donald D. Mathcad computer applications predicting antenna parameters from antenna physical dimensions and ground characteristics. Monterey, Calif: Naval Postgraduate School, 1993.

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3

Environmental Studies Revolving Funds (Canada). Physical approaches to iceberg severity prediction. S.l: s.n, 1986.

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4

Marko, J. R. Physical approaches to iceberg severity prediction. Sidney, B.C: Arctic Sciences Ltd., 1986.

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5

Jochum, Clemens, Martin G. Hicks, and Josef Sunkel, eds. Physical Property Prediction in Organic Chemistry. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1.

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6

Askadskiĭ, A. A. Physical properties of polymers: Prediction and control. Amsterdam: Gordon and Breach Publishers, 1996.

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7

Cody, Weisbach P., ed. Clinical prediction rules: A physical therapy reference manual. Sudbury, Mass: Jones and Bartlett Publishers, 2010.

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8

Lundholm, Steven E. Predicting antenna parameters from antenna physical dimensions. Monterey, Calif: Naval Postgraduate School, 1993.

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9

1939-, Wyss Max, Shimazaki K. 1946-, and Ito Akihiko, eds. Seismicity patterns, their statistical significance and physical meaning. Basel: Birkhäuser Verlag, 1999.

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10

Sinclair, H. R. Physical root restriction prediction in a mine spoil reclamation protocol. S.l: s.n, 1991.

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11

Keilis-Borok, Vladimir I. Nonlinear Dynamics of the Lithosphere and Earthquake Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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12

Skelton, R. P. High Temperature Fatigue: Properties and Prediction. Dordrecht: Springer Netherlands, 1987.

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13

Tsuchiya, Yoshito. Tsunami: Progress in Prediction, Disaster Prevention and Warning. Dordrecht: Springer Netherlands, 1995.

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14

Nicholson, Sean. Physician income prediction errors: Sources and implications for behavior. Cambridge, MA: National Bureau of Economic Research, 2002.

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15

service), SpringerLink (Online, ed. Designing Quantitative Experiments: Prediction Analysis. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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16

Sosa, Matias. Engineering physics and mechanics: Analyses, prediction, and applications. Hauppauge, N.Y: Nova Science Publishers, 2010.

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17

Pham, Duc Truong. Neural Networks for Identification, Prediction and Control. London: Springer London, 1995.

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18

Fasman, Gerald D. Prediction of Protein Structure and the Principles of Protein Conformation. Boston, MA: Springer US, 1989.

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19

Baus, Marc. Observation, Prediction and Simulation of Phase Transitions in Complex Fluids. Dordrecht: Springer Netherlands, 1995.

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20

Kravt͡sov, I͡U A. Limits of Predictability. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993.

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21

Baluch, Issa. Transport Logistics: Past, Present and Predictions. Winning Books, 2005.

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22

Bishop, Paul. Adapted Physical Education: A Comprehensive Resource Manual of Definition, Assessment, Programming and Future Predictions. Educational Systems Associates, Incorporated, 1988.

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23

1950-, Bishop Paul L., and Educational Systems Associates, eds. Adapted physical education: A comprehensive resource manual of definition, assessment, programming and future predictions. Kearney, NE: Educational Systems Associates, 1988.

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24

Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190676889.013.16.

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Анотація:
As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.
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25

Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190699420.013.16.

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Анотація:
As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.
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26

United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch and University of Dayton. Research Institute, eds. The physical and empirical basis for a specific clear-air turbulence risk index. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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27

United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch. and University of Dayton. Research Institute., eds. The physical and empirical basis for a specific clear-air turbulence risk index. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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28

United States. National Aeronautics and Space Administration., ed. The physical and empirical basis for a specific clear-air turbulence risk index: Final report. Dayton, Ohio: University of Dayton, Research Institute, 1985.

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29

K, Lambert, and Lewis Research Center, eds. Physical optics for oven-plate scattering prediction. Urbana, Ill: University of Illinios, 1991.

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30

K, Lambert, and Lewis Research Center, eds. Physical optics for oven-plate scattering prediction. Urbana, Ill: University of Illinios, 1991.

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31

Mora, S., and Y. Pomeau. Capillarity with solids. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198789352.003.0007.

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Анотація:
Capillary phenomena occurring on soft solid interfaces are discussed over this lecture. The main goal is to show how a variational approach provides a deep understanding of the static effects coming from the self-capillarity of elastic solids. After an introduction, the general framework is introduced and then various situations are discussed. In each case, the physical phenomena are first briefly introduced, a theoretical analysis is presented, and then the predictions are compared with experiments when available. This lecture is intended as an introduction rather than as a comprehensive review. Demonstrations are simplified as much as possible thanks to physically relevant assumptions (symmetric problems, two-dimensional problems, etc.). The aim is to highlight the main physical ingredients. References are included throughout the text for readers desiring a more in-depth treatment.
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32

B, Beck F., Cockrell C. R, and Langley Research Center, eds. Modeling 3-D objects with planar surfaces for prediction of electromagnetic scattering. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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33

B, Beck F., Cockrell C. R, and Langley Research Center, eds. Modeling 3-D objects with planar surfaces for prediction of electromagnetic scattering. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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34

Modeling 3-D objects with planar surfaces for prediction of electromagnetic scattering. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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35

B, Beck F., Cockrell C. R, and Langley Research Center, eds. Modeling 3-D objects with planar surfaces for prediction of electromagnetic scattering. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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36

Predicting Motion (The Physical World). Taylor & Francis, 2000.

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37

Paul, Weingartner, and Schurz Gerhard, eds. Law and prediction in the light of chaos research. Berlin: Springer, 1996.

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38

Myrvold, Wayne C. Beyond Chance and Credence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198865094.001.0001.

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Анотація:
Probability concepts permeate physics. This is obvious in statistical mechanics, in which probabilities appear explicitly. But even in cases when predictions are made with near-certainty, there is are implicit probabilistic assumptions in play, as it is assumed that molecular fluctuations can be neglected. How are we to understand these probabilistic concepts? This book offers a fresh look at these familiar topics, urging readers to see them in a new light. It argues that the traditional choices between probabilities as objective chances or degrees of belief is too limiting, and introduces a new concept, called epistemic chances, that combines physical and epistemic considerations. Thinking of probabilities in this way solves some of the puzzles associated with the use of probability and statistical mechanics. The book includes some history of discussions of probability, from the eighteenth to the twentieth century, and introductions to conceptual issues in thermodynamics and statistical mechanics. It should be of interest to philosophers interested in probability, and to physicists and philosophers of physics interested in understanding how probabilistic concepts apply to the physical world.
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39

Eisenkraft, Arthur. Active physics: Predictions. It's About Time, 1999.

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40

Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.

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Анотація:
Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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41

Weingartner, Paul, and Gerhard Schurz. Law and Prediction in the Light of Chaos Research. Springer, 2013.

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42

Lange, Matthias, and Ulrich Focken. Physical Approach to Short-Term Wind Power Prediction. Springer, 2009.

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43

Physical Approach to Short-Term Wind Power Prediction. Springer, 2005.

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44

Lange, Matthias, and Ulrich Focken. Physical Approach to Short-Term Wind Power Prediction. Springer Berlin / Heidelberg, 2010.

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45

Physical Approach to Short-Term Wind Power Prediction. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-31106-8.

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46

Lange, Matthias, and Ulrich Focken. Physical Approach to Short-Term Wind Power Prediction. Springer London, Limited, 2006.

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47

Lambourne, Robert. Predicting Motion. Taylor & Francis Group, 2020.

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48

Lambourne, Robert. Predicting Motion. Taylor & Francis Group, 2019.

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49

Lambourne, Robert. Predicting Motion. Taylor & Francis Group, 2019.

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50

Lambourne, Robert. Predicting Motion. Taylor & Francis Group, 2019.

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