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Books on the topic 'Monte Carlo calculation'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Albers, John. Results of the Monte Carlo calculation of one-and two-dimensional distributions of particles and damage: Ion implanteddopants in silicon. National Bureau of Standards, 1987.

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2

Albers, John. Results of the Monte Carlo calculation of one- and two-dimensional distributions of particles and damage: Ion implanted dopants in silicon. U.S. Dept. of Commerce, National Bureau of Standards, 1987.

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3

Till, E. Calculation of the radiation transport in rock salt using Monte Carlo methods: Final report (HAW Project). GSF-Forschungszentrum für Umwelt und Gesundheit, Institut für Strahlenschutz, 1994.

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4

László, Koblinger, ed. Monte Carlo particle transport methods: Neutron and photon calculations. CRC Press, 1991.

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5

Zhang, Shiwei. Exact Monte Carlo calculations for fermions on a parallel machine. Cornell Theory Center, Cornell University, 1993.

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6

Filippi, Claudia. Multiconfiguration wavefunctions for quantum Monte Carlo calculations of first-row diatomic molecules. Cornell Theory Center, Cornell University, 1996.

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7

Mehta, Shailesh. The Monte Carlo approach to calculating radial distribution functions in dense plasmas. University of Birmingham, 1995.

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8

Hulse, Paul. Applying genetic algorithms to importance map generation for Monte Carlo tracking shielding calculations. University of Salford, 1994.

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9

Jansen, Jan Theo Maria. Monte Carlo calculations in diagnostic radiology: Dose conversion factors and risk benefit analyses = Monte Carlo berekeningen in de radiodiagnostiek : dosis conversiefactoren en risico baten analyses. Rijkuniversiteit te Leiden, 1998.

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10

Whitlock, Paula A. Green's function Monte Carlo calculations for 4He using the shadow wave function as importance function. Cornell Theory Center, Cornell University, 1990.

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11

Wang, Lilie. Evaluations of dose distribution calculations in a commercial radiation treatment planning system by Monte Carlo simulation. Laurentian University, School of Graduate Studies, 2005.

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12

Sentā, Ōarai Kōgaku. Optimization of Monte Carlo methods for calculational predictions of dosimetry measurements in "Joyo" MK-III core: [kenkyū hōkoku]. Japan Nuclear Cycle Development Institute, 2005.

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13

Allen, Michael P., and Dominic J. Tildesley. Advanced Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0009.

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This chapter describes the ways in which the Monte Carlo importance sampling method may be adapted to improve the calculation of ensemble averages, particularly those associated with free energy differences. These approaches include umbrella sampling, non-Boltzmann sampling, the Wang–Landau method, and nested sampling. In addition, a range of special techniques have been developed to accelerate the simulation of flexible molecules, such as polymers. These approaches are illustrated with scientific examples and program code. The chapter also explains the analysis of such simulations using techniques such as weighted histograms, and acceptance ratio calculations. Practical advice on selection of methods, parameters, and the direction in which to make comparisons, are given. Monte Carlo methods for modelling phase equilibria and chemical reactions at equilibrium are described.
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14

Jensen, Daniel Christian. Monte Carlo calculation of electron multiple scattering in thin foils. 1988.

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15

R, Kramer, ed. The Calculation of dose from external photon exposures using reference human phantoms and Monte Carlo methods. Gesellschaft für Strahlen- und Umweltforschung, 1986.

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16

Markov Chains Analytic And Monte Carlo Computations. John Wiley & Sons Australia Ltd, 2014.

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17

Graham, Carl. Markov Chains: Analytic and Monte Carlo Computations. Wiley & Sons, Incorporated, John, 2014.

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18

Graham, Carl. Markov Chains: Analytic and Monte Carlo Computations. Wiley & Sons, Incorporated, John, 2014.

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19

Michael, Ljungberg, Strand Sven-Erik, and King Michael A, eds. Monte Carlo calculations in nuclear medicine: Applications in diagnostic imaging. Institute of Physics Pub., 1998.

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20

(Editor), H. Zaidi, and G. Sgouros (Editor), eds. Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine. Taylor & Francis, 2002.

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21

Zaidi, H., and G. Sgouros. Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine. Taylor & Francis Group, 2002.

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22

Zaidi, Habib, and George Sgouros, eds. Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine. IOP Publishing Ltd, 2003. http://dx.doi.org/10.1887/0750308168.

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23

Lux, Iván, and László Koblinger. Monte Carlo Particle Transport Methods: Neutron and Photon Calculations. CRC Press, 2018. http://dx.doi.org/10.1201/9781351074834.

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24

(Editor), M. Ljungberg, S. E. Strand (Editor), and M. A. King (Editor), eds. Monte Carlo Calculations in Nuclear Medicine: Applications in Diagnostic Imaging (Medical Sciences Series). Taylor & Francis, 1998.

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25

Miles, Todd L. Monte Carlo uncertainty reliability and isotope production calculations for a fast reactor. 1991.

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26

Monte Carlo Calculations in Nuclear Medicine: Applications in Diagnostic Imaging. Taylor & Francis Group, 2012.

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27

Wilson, James Coleman. The development of a Monte Carlo calculation for the accurate analysis of photon, electron and positron transport problems in high-Z media at energies to 10 MeV and its application to small bismuth germanium oxide scintillation detectors. 1987.

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28

Allen, Michael P., and Dominic J. Tildesley. Quantum simulations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0013.

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This chapter covers the introduction of quantum mechanics into computer simulation methods. The chapter begins by explaining how electronic degrees of freedom may be handled in an ab initio fashion and how the resulting forces are included in the classical dynamics of the nuclei. The technique for combining the ab initio molecular dynamics of a small region, with classical dynamics or molecular mechanics applied to the surrounding environment, is explained. There is a section on handling quantum degrees of freedom, such as low-mass nuclei, by discretized path integral methods, complete with practical code examples. The problem of calculating quantum time correlation functions is addressed. Ground-state quantum Monte Carlo methods are explained, and the chapter concludes with a forward look to the future development of such techniques particularly to systems that include excited electronic states.
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29

Donovan, Therese, and Ruth M. Mickey. Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.001.0001.

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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices.
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30

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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31

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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