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1

Keane, Andrew John. Statistical energy analysis of engineering structures. Uxbridge: Brunel University, 1988.

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2

James, P. P. Weak coupling in statistical energy analysis. Southampton, England: University of Southampton, Institute of Sound and Vibration Research, 1994.

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3

Fahy, F. J., and W. G. Price, eds. IUTAM Symposium on Statistical Energy Analysis. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9173-7.

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4

G, DeJong Richard, and Lyon Richard H, eds. Theory and application of statistical energy analysis. 2nd ed. Boston: Butterworth-Heinemann, 1995.

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5

Craik, Robert J. M. Sound transmission through buildings: Using statistical energy analysis. Aldershot, England: Gower, 1996.

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6

Quantum ecology: Energy structure and its analysis. London: Scada Publishing, 2013.

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7

Orlóci, László. Quantum ecology: Energy structure and its analysis. 2nd ed. London: SCADA Publishing, 2014.

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8

1945-, Chung C. F., Fabbri Andrea G, and Sinding-Larsen Richard, eds. Quantitative analysis of mineral and energy resources. Dordrecht: D. Reidel Pub. Co, 1988.

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9

James, P. P. Evolution of the energy impulse response in the case of two very weakly coupled systems: a mathematical model. [S.l.]: University of Southampton, Institute of Sound and Vibration Research, 1995.

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10

Reddy, T. Agami. Applied data analysis and modeling for energy engineers and scientists. New York: Springer, 2011.

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11

Wiser, Ryan. Utility green pricing programs: A statistical analysis of program effectiveness. Berkeley, California: Ernest Orlando Lawrence Berkeley National Laboratory, 2004.

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12

IUTAM Symposium on Statistical Energy Analysis (1997 Southampton, U.K.). IUTAM Symposium on Statistical Energy Analysis: Proceedings of the IUTAM Symposium held in Southampton, UK, 8-11 July 1997. Dordrecht: Kluwer Academic Publishers, 1999.

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13

Popławski, Tomasz. Methods of analysis and forecast of power engineering load variation in the conditions of energy market transformation. Częstochowa: Publishing Office of Czestochowa University of Technology, 2009.

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14

Johnston, Douglas H. A radiographic and statistical analysis of the effects of high energy ionizing radiation upon human tooth germ development. [Toronto: Faculty of Dentistry, University of Toronto], 1988.

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15

Fahy, F. J. IUTAM Symposium on Statistical Energy Analysis: Proceedings of the IUTAM Symposium held in Southampton, U.K., 8-11 July 1997. Dordrecht: Springer Netherlands, 1999.

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16

F, Peretti Linda, and United States. National Aeronautics and Space Administration., eds. Asymptotic modal analysis and statistical energy analysis. [Washington, DC: National Aeronautics and Space Administration, 1990.

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17

United States. National Aeronautics and Space Administration., ed. Asymptotic model analysis and statistical energy analysis. [Washington, DC: National Aeronautics and Space Administration, 1992.

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18

Bot, A. Le. Foundation of Statistical Energy Analysis in Vibroacoustics. Oxford University Press, 2015.

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19

Theory and Application of Statistical Energy Analysis. Elsevier, 1995. http://dx.doi.org/10.1016/c2009-0-26747-x.

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20

Hsu, K. H. Statistical Energy Analysis/Asme Nca Vol. 3./G00403. Amer Society of Mechanical, 1987.

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21

Lyon, Richard H. Statistical Energy Analysis of Dynamical Systems: Theory and Applications. MIT Press, 2003.

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22

J, Keane A., and Price W. G, eds. Statistical energy analysis: An overview, with applications in sturctural dynamics. Cambridge, U.K: Cambridge University Press, 1997.

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23

(Editor), A. J. Keane, and W. G. Price (Editor), eds. Statistical Energy Analysis: An Overview, with Applications in Structural Dynamics. Cambridge University Press, 2005.

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24

(Editor), A. J. Keane, and W. G. Price (Editor), eds. Statistical Energy Analysis: An Overview, with Applications in Structural Dynamics. Cambridge University Press, 1997.

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25

(Editor), F. J. Fahy, and W. G. Price (Editor), eds. IUTAM Symposium on Statistical Energy Analysis (Solid Mechanics and Its Applications). Springer, 1998.

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26

Chortis, Dimitris I. Structural Analysis of Composite Wind Turbine Blades: Nonlinear Mechanics and Finite Element Models with Material Damping. 2013.

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27

Reddy, T. Agami. Applied Data Analysis and Modeling for Energy Engineers and Scientists. Springer, 2014.

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28

Data Analysis In High Energy Physics A Practical Guide To Statistical Methods. Wiley-VCH Verlag GmbH, 2013.

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29

H, Wiser Ryan, Lawrence Berkeley National Laboratory. Environmental Energy Technologies Division, and National Renewable Energy Laboratory (U.S.), eds. Utility green pricing programs: A statistical analysis of program effectiveness. Berkeley, Calif: Ernest Orlando Lawrence Berkeley National Laboratory ; Golden, Colo., 2004.

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30

Power flow analysis of two coupled plates with arbitrary characteristics. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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31

United States. National Aeronautics and Space Administration., ed. Power flow analysis of two coupled plates with arbitrary characteristics: Fourth semi-annual report, grant number NAG-1-685. Boca Raton, Fla: Florida Atlantic University, College of Engineering, Department of Ocean Engineering, Center for Acoustics and Vibrations, 1988.

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32

L, Krantz T., and United States. National Aeronautics and Space Administration., eds. Minimization of the vibration energy of thin-plate structures and the application to the reduction of gearbox vibration. [Washington, DC: National Aeronautics and Space Administration, 1995.

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33

L, Krantz T., and United States. National Aeronautics and Space Administration., eds. Minimization of the vibration energy of thin-plate structures and the application to the reduction of gearbox vibration. [Washington, DC: National Aeronautics and Space Administration, 1995.

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34

Power flow as a complement to statistical energy analysis and finite element analysis: First semi-annual report, grant no. NAG-1-685. Boca Raton, Fla: Florida Atlantic University, College of Engineering, Dept. of Ocean Engineering, Center for Acoustics and Vibration, 1987.

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35

Effects of Energy Needs and Expenditures on United States Public Schools: Statistical Analysis Report, May 2003. USGOV, 2003.

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36

United States. National Aeronautics and Space Administration., ed. Parametric and experimental analysis using a power flow approach. Boca Raton, Florida: Florida Atlantic University, College of Engineering, Dept. of Ocean Engineering, Center for Acoustics and Vibrations, 1988.

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37

Center, Langley Research, ed. Parametric and experimental analysis using a power flow approach. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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38

H, Hsu K., Nefske D. J, Akay A, American Society of Mechanical Engineers. Winter Meeting, and American Society of Mechanical Engineers. Numerical Techniques in Acoustics Committee., eds. Statistical energy analysis: Presented at the Winter Annual Meeting of the American Society of Mechanical Engineers, Boston, Massachusetts, December 13-18, 1987. New York, N.Y. (345 E. 47th St., New York 10017): American Society of Mechanical Engineers, 1987.

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39

United States. Office of Educational Research and Improvement., ed. Characteristics Of The 100 Largest Public Elementary And Secondary School Districts In The United States: 1993-94... Statistical Analysis Report... U.S. Dept. Of Energy... June 1996. [S.l: s.n., 1998.

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40

Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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41

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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