Relevant bibliographies by topics / Monte Carlo simulation model

Academic literature on the topic 'Monte Carlo simulation model'

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Published: 9 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "Monte Carlo simulation model"

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Suzuki, SHO, NAOKI Takano, and MITSUTERU ASAI. "F406 Monte Carlo Simulation of dynamic problem using Model Order Reduction Technique." Proceedings of The Computational Mechanics Conference 2011.24 (2011): _F—58_—_F—59_. http://dx.doi.org/10.1299/jsmecmd.2011.24._f-58_.

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Bravyi, Sergey. "Monte Carlo simulation of stoquastic Hamiltonians." Quantum Information and Computation 15, no. 13&14 (October 2015): 1122–40. http://dx.doi.org/10.26421/qic15.13-14-3.

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Stoquastic Hamiltonians are characterized by the property that their off-diagonal matrix elements in the standard product basis are real and non-positive. Many interesting quantum models fall into this class including the Transverse field Ising Model (TIM), the Heisenberg model on bipartite graphs, and the bosonic Hubbard model. Here we consider the problem of estimating the ground state energy of a local stoquastic Hamiltonian $H$ with a promise that the ground state of $H$ has a non-negligible correlation with some `guiding' state that admits a concise classical description. A formalized version of this problem called Guided Stoquastic Hamiltonian is shown to be complete for the complexity class $\MA$ (a probabilistic analogue of $\NP$). To prove this result we employ the Projection Monte Carlo algorithm with a variable number of walkers. Secondly, we show that the ground state and thermal equilibrium properties of the ferromagnetic TIM can be simulated in polynomial time on a classical probabilistic computer. This result is based on the approximation algorithm for the classical ferromagnetic Ising model due to Jerrum and Sinclair (1993).
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Koerkamp, Bas Groot, Theo Stijnen, Milton C. Weinstein, and M. G. Myriam Hunink. "The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models." Medical Decision Making 31, no. 4 (October 25, 2010): 650–61. http://dx.doi.org/10.1177/0272989x10381282.

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The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity.
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Jin, Wen-Long, and Wilfred W. Recker. "Monte Carlo Simulation Model of Intervehicle Communication." Transportation Research Record: Journal of the Transportation Research Board 2000, no. 1 (January 2007): 8–15. http://dx.doi.org/10.3141/2000-02.

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Panov, Yu D., A. S. Moskvin, A. A. Chikov, and V. A. Ulitko. "Monte Carlo simulation of a model cuprate." Journal of Physics: Conference Series 2043, no. 1 (October 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2043/1/012007.

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Zhang, Ji-xiang, Hui Wen, and Yun-teng Liu. "Monte carlo model in metal recrystallization simulation." Journal of Shanghai Jiaotong University (Science) 16, no. 3 (June 2011): 337–42. http://dx.doi.org/10.1007/s12204-011-1156-x.

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Saly, Rudolf. "Monte Carlo simulation of lattice Skyrme model." Computer Physics Communications 36, no. 4 (June 1985): 417–22. http://dx.doi.org/10.1016/0010-4655(85)90031-1.

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Takahashi, Akiyuki, Naoki Soneda, and Masanori Kikuchi. "Computer Simulation of Microstructure Evolution of Fe-Cu Alloy during Thermal Ageing." Key Engineering Materials 306-308 (March 2006): 917–22. http://dx.doi.org/10.4028/www.scientific.net/kem.306-308.917.

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This paper describes a computer simulation of thermal ageing process in Fe-Cu alloy. In order to perform accurate numerical simulation, firstly, we make numerical models of the diffusion and dissociation of Cu and Cu-vacancy clusters. This modeling was performed with kinetic lattice Monte Carlo method, which allows us to perform long-time simulation of vacancy diffusion in Fe-Cu dilute alloy. The model is input to the kinetic Monte Carlo method, and then, we performed the kinetic Monte Carlo simulation of the thermal ageing in the Fe-Cu alloy. The results of the KMC simulations tell us that the our new models describes well the rate and kinetics of the diffusion and dissociation of Cu and Cu-vacancy clusters, and works well in the kinetic Monte Carlo simulations. Finally, we discussed the further application of these numerical models.
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APAJA, VESA, and OLAV F. SYLJUÅSEN. "MONTE CARLO SIMULATION OF BOSON LATTICES." International Journal of Modern Physics B 20, no. 30n31 (December 20, 2006): 5113–16. http://dx.doi.org/10.1142/s0217979206036168.

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Boson lattices are theoretically well described by the Hubbard model. The basic model and its variants can be effectively simulated using Monte Carlo techniques. We describe two newly developed approaches, the Stochastic Series Expansion (SSE) with directed loop updates and continuous–time Diffusion Monte Carlo (CTDMC). SSE is a formulation of the finite temperature partition function as a stochastic sampling over product terms. Directed loops is a general framework to implement this stochastic sampling in a non–local fashion while maintaining detailed balance. CTDMC is well suited to finding exact ground–state properties, applicable to any lattice model not suffering from the sign problem; for a lattice model the evolution of the wave function can be performed in continuous time without any time discretization error. Both the directed loop algorithm and the CTDMC are important recent advances in development of computational methods. Here we present results for a Hubbard model for anti–ferromagnetic spin–1 bosons in one dimensions, and show evidence for a dimerized ground state in the lowest Mott lobe.
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HATANO, NAOMICHI. "MONTE CARLO SIMULATION OF RANDOM BOSON HUBBARD MODEL." International Journal of Modern Physics C 07, no. 03 (June 1996): 449–56. http://dx.doi.org/10.1142/s0129183196000405.

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A Monte Carlo algorithm for the random Boson Hubbard model is reported. The analytic expression of the matrix elements is presented, and the ergodicity of the Monte Carlo flips is discussed. The results in one dimension supports a previously proposed perturbational scaling argument.
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Dissertations / Theses on the topic "Monte Carlo simulation model"

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Hanlon, Peter E. "A retirement planning model using Monte Carlo simulation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA386389.

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Wang, Dong-Mei. "Monte Carlo simulations for complex option pricing." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/monte-carlo-simulations-for-complex-option-pricing(a908ec86-2fb2-4d5d-83e5-9bff78033edd).html.

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The thesis focuses on pricing complex options using Monte Carlo simulations. Due to the versatility of the Monte Carlo method, we are able to evaluate option prices with various underlying asset models: jump diffusion models, illiquidity models, stochastic volatility and so on. Both European options and Bermudan options are studied in this thesis.For the jump diffusion model in Merton (1973), we demonstrate European and Bermudan option pricing by the Monte Carlo scheme and extend this to multiple underlying assets; furthermore, we analyse the effect of stochastic volatility.For the illiquidity model in the spirit of Glover (2008), we model the illiquidity impact on option pricing in the simulation study. The four models considered are: the first order feedback model with constant illiquidity and stochastic illiquidity; the full feedback model with constant illiquidity and stochastic illiquidity. We provide detailed explanations for the present of path failures when simulating the underlying asset price movement and suggest some measures to overcome these difficulties.
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Kheirollah, Amir. "Monte Carlo Simulation of Heston Model in MATLAB GUI." Thesis, Mälardalen University, Mälardalen University, Department of Mathematics and Physics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-4253.

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In the Black-Scholes model, the volatility considered being deterministic and it causes some

inefficiencies and trends in pricing options. It has been proposed by many authors that the

volatility should be modelled by a stochastic process. Heston Model is one solution to this

problem. To simulate the Heston Model we should be able to overcome the correlation

between asset price and the stochastic volatility. This paper considers a solution to this issue.

A review of the Heston Model presented in this paper and after modelling some investigations

are done on the applet.

Also the application of this model on some type of options has programmed by MATLAB

Graphical User Interface (GUI).

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Smith, Graham. "The measurement of free energy by Monte Carlo computer simulation." Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/6466.

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One of the most important problems in statistical mechanics is the measurement of free energies, these being the quantities that determine the direction of chemical reactions and--the concern of this thesis--the location of phase transitions. While Monte Carlo (MC) computer simulation is a well-established and invaluable aid in statistical mechanical calculations, it is well known that, in its most commonly-practised form (where samples are generated from the Boltzmann distribution), it fails if applied directly to the free energy problem. This failure occurs because the measurement of free energies requires a much more extensive exploration of the system's configuration space than do most statistical mechanical calculations: configurations which have a very low Boltzmann probability make a substantial contribution to the free energy, and the important regions of configuration space may be separated by potential barriers. We begin the thesis with an introduction, and then give a review of the very substantial literature that the problem of the MC measurement of free energy has produced, explaining and classifying the various different approaches that have been adopted. We then proceed to present the results of our own investigations. First, we investigate methods in which the configurations of the system are sampled from a distribution other than the Boltzmann distribution, concentrating in particular on a recently developed technique known as the multicanonical ensemble. The principal difficulty in using the multicanonical ensemble is the difficulty of constructing it: implicit in it is at least partial knowledge of the very free energy that we are trying to measure, and so to produce it requires an iterative process. Therefore we study this iterative process, using Bayesian inference to extend the usual method of MC data analysis, and introducing a new MC method in which inferences are made based not on the macrostates visited by the simulation but on the transitions made between them. We present a detailed comparison between the multicanonical ensemble and the traditional method of free energy measurement, thermodynamic integration, and use the former to make a high-accuracy investigation of the critical magnetisation distribution of the 2d Ising model from the scaling region all the way to saturation. We also make some comments on the possibility of going beyond the multicanonical ensemble to `optimal' MC sampling. Second, we investigate an isostructural solid-solid phase transition in a system consisting of hard spheres with a square-well attractive potential. Recent work, which we have confirmed, suggests that this transition exists when the range of the attraction is very small (width of attractive potential/ hard core diameter ~ 0.01). First we study this system using a method of free energy measurement in which the square-well potential is smoothly transformed into that of the Einstein solid. This enables a direct comparison of a multicanonical-like method with thermodynamic integration. Then we perform extensive simulations using a different, purely multicanonical approach, which enables the direct connection of the two coexisting phases. It is found that the measurement of transition probabilities is again advantageous for the generation of the multicanonical ensemble, and can even be used to produce the final estimators. Some of the work presented in this thesis has been published or accepted for publication: the references are G. R. Smith & A. D. Bruce, A Study of the Multicanonical Monte Carlo Method, J. Phys. A. 28, 6623 (1995). [reference details doi:10.1088/0305-4470/28/23/015] G. R. Smith & A. D. Bruce, Multicanonical Monte Carlo Study of a Structural Phase Transition, to be published in Europhys. Lett. [reference details Europhys. Lett. 34, 91 (1996) doi:10.1209/epl/i1996-00421-1] G. R. Smith & A. D. Bruce, Multicanonical Monte Carlo Study of Solid-Solid Phase Coexistence in a Model Colloid, to be published in Phys. Rev. E [reference details Phys. Rev. E 53, 6530–6543 (1996) doi:10.1103/PhysRevE.53.6530].
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Ventura, Marcelo dos Santos. "Monte Carlo simulation studies in log-symmetric regressions." Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/8278.

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Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG
This work deals with two Monte Carlo simulation studies in log-symmetric regression models, which are particularly useful for the cases when the response variable is continuous, strictly positive and asymmetric, with the possibility of the existence of atypical observations. In log- symmetric regression models, the distribution of the random errors multiplicative belongs to the log-symmetric class, which encompasses log-normal, log- Student-t, log-power- exponential, log-slash, log-hyperbolic distributions, among others. The first simulation study has as objective to examine the performance for the maximum-likelihood estimators of the model parameters, where various scenarios are considered. The objective of the second simulation study is to investigate the accuracy of popular information criteria as AIC, BIC, HQIC and their respective corrected versions. As illustration, a movie data set obtained and assembled for this dissertation is analyzed to compare log-symmetric models with the normal linear model and to obtain the best model by using the mentioned information criteria.
Este trabalho aborda dois estudos de simulação de Monte Carlo em modelos de regressão log- simétricos, os quais são particularmente úteis para os casos em que a variável resposta é contínua, estritamente positiva e assimétrica, com possibilidade da existência de observações atípicas. Nos modelos de regressão log-simétricos, a distribuição dos erros aleatórios multiplicativos pertence à classe log-simétrica, a qual engloba as distribuições log-normal, log-Student- t, log-exponencial- potência, log-slash, log-hyperbólica, entre outras. O primeiro estudo de simulação tem como objetivo examinar o desempenho dos estimadores de máxima verossimilhança desses modelos, onde vários cenários são considerados. No segundo estudo de simulação o objetivo é investigar a eficácia critérios de informação populares como AIC, BIC, HQIC e suas respectivas versões corrigidas. Como ilustração, um conjunto de dados de filmes obtido e montado para essa dissertação é analisado para comparar os modelos de regressão log-simétricos com o modelo linear normal e para obter o melhor modelo utilizando os critérios de informação mencionados.
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Johansson, Sam. "Efficient Monte Carlo Simulation for Counterparty Credit Risk Modeling." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252566.

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In this paper, Monte Carlo simulation for CCR (Counterparty Credit Risk) modeling is investigated. A jump-diffusion model, Bates' model, is used to describe the price process of an asset, and the counterparty default probability is described by a stochastic intensity model with constant intensity. In combination with Monte Carlo simulation, the variance reduction technique importance sampling is used in an attempt to make the simulations more efficient. Importance sampling is used for simulation of both the asset price and, for CVA (Credit Valuation Adjustment) estimation, the default time. CVA is simulated for both European and Bermudan options. It is shown that a significant variance reduction can be achieved by utilizing importance sampling for asset price simulations. It is also shown that a significant variance reduction for CVA simulation can be achieved for counterparties with small default probabilities by employing importance sampling for the default times. This holds for both European and Bermudan options. Furthermore, the regression based method least squares Monte Carlo is used to estimate the price of a Bermudan option, resulting in CVA estimates that lie within an interval of feasible values. Finally, some topics of further research are suggested.
I denna rapport undersöks Monte Carlo-simuleringar för motpartskreditrisk. En jump-diffusion-modell, Bates modell, används för att beskriva prisprocessen hos en tillgång, och sannolikheten att motparten drabbas av insolvens beskrivs av en stokastisk intensitetsmodell med konstant intensitet. Tillsammans med Monte Carlo-simuleringar används variansreduktionstekinken importance sampling i ett försök att effektivisera simuleringarna. Importance sampling används för simulering av både tillgångens pris och, för estimering av CVA (Credit Valuation Adjustment), tidpunkten för insolvens. CVA simuleras för både europeiska optioner och Bermuda-optioner. Det visas att en signifikant variansreduktion kan uppnås genom att använda importance sampling för simuleringen av tillgångens pris. Det visas även att en signifikant variansreduktion för CVA-simulering kan uppnås för motparter med små sannolikheter att drabbas av insolvens genom att använda importance sampling för simulering av tidpunkter för insolvens. Detta gäller både europeiska optioner och Bermuda-optioner. Vidare, används regressionsmetoden least squares Monte Carlo för att estimera priset av en Bermuda-option, vilket resulterar i CVA-estimat som ligger inom ett intervall av rimliga värden. Slutligen föreslås några ämnen för ytterligare forskning.
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Flores, Garth. "A stochastic model for sewer base flows using Monte Carlo simulation." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96692.

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Thesis (MSc)--Stellenbosch University, 2015.
ENGLISH ABSTRACT: This thesis deals with understanding and quantifying the components that make up sewage base flows (SBF). SBF is a steady flow that is ubiquitous in sewers, and is clearly seen when measuring the flow rate in the sewer between 03:00 and 04:00. The components of SBF are: ● return flow from residential night use, ● return flow from leaking plumbing, ● groundwater infiltration, ● stormwater inflow. By understanding each component of SBF, this research can answer the burning question as to how much of the SBF was due to plumbing leaks on residential properties. While previous work on SBF had been done, the work focused on groundwater ingress and stormwater inflows, and thus not much had been said about plumbing leaks. Furthermore, previous work focused on SBF as an isolated sewer related topic, whereas this research integrated SBF as both a sewer related topic and water conservation and demand management (WCDM) topic. Due to the high variability in each of the SBF components, a method of quantifying each component was developed using residential end-use modelling and Monte Carlo simulations. The author constructed the Leakage, Infiltration and Inflow Technique Model (LIFT Model). This stochastic model was built in MS Excel using the @Risk software add-on. The LIFT Model uses probability distributions to model the inflow variability. The results of the stochastic model were analysed and the findings discussed. This research can be used by water utilities as a tool to better understand the SBF in networks. Armed with this knowledge, water utilities could make informed decisions about how to best reduce the high SBF encountered in networks.
AFRIKAANSE OPSOMMING: Hierdie verhandeling bespreek die begrip en berekening van die komponente van riool nagvloei. Die nagvloei was duidelik wanneer die vloei in die rioolstelsel tussen 03:00 en 04:00 gemeet is. Die verskillende komponente van die nagvloei is: ● huishoudelike gebruik, ● lekkende krane en toilette, ● grondwaterinfiltrasie, en ● stormwaterinvloei. ’n Begrip van die komponente van nagvloei kan die brandende vraag van hoeveel nagvloei die gevolg van lekkende krane en toilette is, na aanleiding van die navorsing beantwoord. Vorige werk het op beter begrip van die grondwaterinfiltrasie en stormwaterinvloei gefokus en lekke het nie veel aandag geniet nie. Vorige werk het net op nagvloei as geïsoleerde rioolonderwerp gefokus, terwyl hierdie navorsing nagvloei as ’n onderwerp wat met riool verband hou, sowel as ’n waterverbruik- en behoeftebestuursonderwerp, ondersoek. As gevolg van die groot verskil tussen elk van die komponente van die nagvloei, is ’n metode ontwikkel wat elke komponent kwantifiseer deur gebruik te maak van eindgebruik-modelle en Monte Carlo-simulasies. Die outeur het die Leakage Infiltration and Inflow Technique Model (LIFT-Model) gebou. Hierdie stogastiese model is in MS Excel, met behulp van die @Risk sagtewarebyvoeging gebou. Die LIFT-Model gebruik waarskynlikheidverspreidings om invloeivariasie te modelleer. Die resultate van die stogastiese model is ontleed en die bevindinge bespreek. Hierdie navorsing mag moontlik deur watervoorsieningsmaatskapye as instrument gebruik word om nagvloei in rioolstelsels beter te verstaan. Hierdie nuwe kennis kan watervoorsieningsmaatskapye in staat stel om ingeligte besluite te neem rakende die beste metodes om te volg om nagvloei te verminder.
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Steinke, Tanja. "Ein Monte-Carlo-Modell zur Simulation plasmagespritzter Wärmedämmschichten /." Tönning ; Lübeck Marburg : Der Andere Verl, 2008. http://d-nb.info/989939944/04.

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Rodgers, Anthony C. Bailey Michael P. "ML-Recon simulation model : a Monte Carlo planning aid for Magic Lantern." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA304223.

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Basik, Beata-Marie. "Direct simulation Monte Carlo model of a couette flow of granular materials." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=60433.

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Since life-threatening natural phenomena, such as, snow avalanches and lava flows, and many industrial and agricultural material handling processes may be classified as granular flows, establishing constitutive relationships which model granular flow behaviour is of prime importance. While laboratory experiments attempting to support granular flow theory have been plagued by poor instrumentation, numerical simulations are becoming increasingly helpful in understanding the nature of these flows. The present investigation describes such a simulation developed within the framework of the Direct Simulation Monte Carlo model for rarefied gases presented in Bird (1976) and granular flow kinetic theory according to Lun, et al. (1984). More specifically, the model generates a Couette flow of smooth, inelastic, homogeneous, spherical granular particles. Two different boundary condition models are used to model the flow field's upper and lower boundaries: the Periodic Boundary Condition (PBC) model and the Finite Shear Layer (FSL) model. An essentially uniform shear flow with virtually no slip at the boundaries results from both boundary conditions. Stress and granular temperature results obtained with the PBC and FSL models for the lower range of solids fractions ($ nu < 0.3)$ compare very well with the Lun, et al. (1984) theory. At higher solids fractions, while the total stresses generated with both boundary models are in reasonable agreement with the latter theory and results from other numerical work, higher than expected streaming stresses appear to be compensating for lower than expected collisional stresses; as a result, granular temperature in this range of solids fractions proves to be higher than predicted.
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Books on the topic "Monte Carlo simulation model"

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Monte Carlo simulation of disorderd systems. Singapore: World Scientific, 1992.

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Moglestue, C. Monte Carlo simulation of semiconductor devices. London: Chapman & Hall, 1993.

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Monte Carlo simulation with applications to finance. Boca Raton: CRC Press, 2012.

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Bernd, Meinerzhagen, ed. Hierarchical device simulation: The Monte-Carlo perspective. Wien: Springer, 2003.

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A, Jackson Kenneth. Monte Carlo simulation of the rapid crystallization of bismuth-doped silicon. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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Quantitative risk analysis: A guide to Monte Carlo simulation modelling. Chichester: Wiley, 1996.

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1956-, Lugli P., ed. The Monte Carlo method for semiconductor device simulation. Wein: Springer-Verlag, 1989.

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Rubinstein, Reuven Y. Monte Carlo optimization, simulation, and sensitivity of queuing networks. Malabar, Fla: Krieger Pub. Co., 1992.

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Koura, Katsuhisa. Monte Carlo simulation of rarefied nitrogen gases contained between parallel plates using the statistical inelastic cross-section model. Tokyo, Japan: National Aerospace Laboratory, 1994.

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Geostatistical simulation: Models and algorithms. New York: Springer, 2002.

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Book chapters on the topic "Monte Carlo simulation model"

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Woolard, D. L., H. Tian, M. A. Littlejohn, R. J. Trew, and K. W. Kim. "The Application of Monte Carlo Techniques in Advanced Hydrodynamic Transport Models." In Monte Carlo Device Simulation, 219–66. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7_8.

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Chen, Geng, and Sheng Luo. "Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation." In Monte-Carlo Simulation-Based Statistical Modeling, 347–66. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_16.

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Fukuda, Ryoji, and Kaoru Oka. "Monte-Carlo Simulation of Error Sort Model." In Advances in Intelligent and Soft Computing, 479–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22833-9_58.

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Macedo, Antonini Puppin, and Antonio C. P. Brasil. "A Coupled Monte Carlo/Explicit Euler Method for the Numerical Simulation of a Forest Fire Spreading Model." In Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 333–45. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2552-2_21.

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Irimata, Kyle M., and Jeffrey R. Wilson. "Monte-Carlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models." In Monte-Carlo Simulation-Based Statistical Modeling, 255–83. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_13.

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Schmid, F., C. Stadler, and H. Lange. "Monte Carlo Simulation of Langmuir Monolayer Models." In Springer Proceedings in Physics, 37–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-46851-3_4.

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Novakova, Jana. "Monte Carlo Simulations of the Tile Calorimeter." In Standard Model Measurements with the ATLAS Detector, 15–29. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00810-3_3.

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Liu, Xiang, Tian Chen, Yuanzhang Li, and Hua Liang. "Bootstrap-Based LASSO-Type Selection to Build Generalized Additive Partially Linear Models for High-Dimensional Data." In Monte-Carlo Simulation-Based Statistical Modeling, 405–24. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_18.

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Chatterjee, A. P., and A. Z. Panagiotopoulos. "Monte Carlo Simulations of Model Nonionic Surfactants." In Springer Proceedings in Physics, 211–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59689-6_21.

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Rubino, Gerardo, and Bruno Tuffin. "Markovian Models for Dependability Analysis." In Rare Event Simulation using Monte Carlo Methods, 125–43. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.ch6.

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Conference papers on the topic "Monte Carlo simulation model"

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Chen, Xi, and Enlu Zhou. "Population model-based optimization with sequential Monte Carlo." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721490.

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Feil, Balazs, Sergei Kucherenko, and Nilay Shah. "Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation." In 2009 First International Conference on Advances in System Simulation (SIMUL). IEEE, 2009. http://dx.doi.org/10.1109/simul.2009.34.

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Warner, James, Samantha C. Niemoeller, Luke Morrill, Geoffrey Bomarito, Patrick Leser, William Leser, Robert A. Williams, and Soumyo Dutta. "Multi-Model Monte Carlo Estimators for Trajectory Simulation." In AIAA Scitech 2021 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-0761.

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Martin, Jay, and Timothy Simpson. "A Monte Carlo Simulation of the Kriging Model." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4483.

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M. Hvid, J., and S. B. Nielsen. "Monte Carlo simulation of a simple petroleum expulsion model." In 56th EAEG Meeting. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201410249.

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Fan, Jing. "A Generalized Soft-Sphere Model for Monte Carlo Simulation." In RAREFIED GAS DYNAMICS: 23rd International Symposium. AIP, 2003. http://dx.doi.org/10.1063/1.1581571.

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Korzec, Tomasz, Francesco Knechtli, Ulli Wolff, and Björn Leder. "Monte-Carlo simulation of the chiral Gross-Neveu model." In XXIIIrd International Symposium on Lattice Field Theory. Trieste, Italy: Sissa Medialab, 2005. http://dx.doi.org/10.22323/1.020.0267.

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Bridge, William J., and Adrian Korpel. "Monte Carlo simulation of strong acoustooptic interaction." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.mg3.

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In the conventional approach to strong acoustooptic interaction, the usual model is that of a rectangular sound column illuminated by a plane wave of light. By considering either the coupled modes of the unperturbed medium or the eigenmodes of the perturbed medium, a solution can then be found which predicts discrete plane waves of light as the diffracted orders. This model gives results in reasonable agreement with theory for configurations that approximate it. However, the physical reality of strongly interacting, arbitrary, diffracting sound and light fields is obviously very different from the simple, plane wave-sound column model. Qualitatively, any discrete orders predicted by the latter model can be explained in terms of multiple scattering in the physics based approach.1 Quantitatively, the solution must be framed in terms of Feynman diagrams and path integrals.2 A quantum mechanical interpretation of this formalism is one of photon transitions induced by the sound field. We report here on recent successful numerical experiments that simulate this quantum mechanical model by Monte Carlo methods using a classical computer.
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De Bellis, Lisa, Ravi S. Prasher, and Patrick E. Phelan. "Predicting Thermal Boundary Resistance Using Monte Carlo Simulation." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0708.

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Abstract The Acoustic Mismatch Model (AMM) and the Diffuse Mismatch Model (DMM) have traditionally been used to predict the thermal boundary resistance, Rb, across the interface of two adjoining materials at temperatures well below the Debye temperatures of the materials in question. Both models, however, fall short when compared to experimental data. The development of these models involves limiting assumptions in order to simplify the mathematical evaluation. A Monte Carlo (MC) Model is proposed and developed as a compliment to the AMM and DMM models. Using the statistical approach eliminates the need of addressing complicated expressions, thereby allowing us to lift some of the limiting assumptions. Furthermore, for the first time, the AMM and DMM are combined into a single, mixed model which determines Rb based on a net heat transfer calculated from both specular and diffuse transmission. As expected, the results in this instance lay between those of the AMM and DMM models.
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Steinke, T., and M. Bäker. "Monte Carlo Simulation of Thermal Sprayed Coatings." In ITSC2006, edited by B. R. Marple, M. M. Hyland, Y. C. Lau, R. S. Lima, and J. Voyer. ASM International, 2006. http://dx.doi.org/10.31399/asm.cp.itsc2006p0329.

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Abstract The investigation of the splashing and spreading behaviour of an impacting powder particle during the plasma spraying process is a field of research often performed with the help of simulations. To simulate thermal sprayed coatings at large scale, the Monte Carlo method is a useful technology. In the Monte Carlo model presented in this manuscript, spreading starts from the particle impact point and continues over the coating so that the behaviour at steps or other surface structures can be taken into account. The discretisation of the model is only lateral so that the thickness of the coating perpendicular to the substrate can take arbitrary values. The rules for particle spreading and deposition used in the Monte Carlo model are derived from a fluid dynamics model. For a two-dimensional simulation the Monte Carlo results provides good agreement with the fluid dynamics results.
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Reports on the topic "Monte Carlo simulation model"

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Boyd, Iain D. A Threshold Line Dissociation Model for the Direct Simulation Monte Carlo Method,. Fort Belvoir, VA: Defense Technical Information Center, May 1996. http://dx.doi.org/10.21236/ada324950.

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Clegg, Benjamin Wyatt, David H. Collins, Jr., and Aparna V. Huzurbazar. Petri Nets for Adversarial Models using Monte Carlo Simulation. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1473775.

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Pacheco, Jose, Zakari Eckert, Russell Hooper, Melissa Finley, and Ronald Manginell. A Novel use of Direct Simulation Monte-Carlo to Model Dynamics of COVID-19 Pandemic Spread. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1648851.

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Cai, D., N. Gronbech-Jensen, C. M. Snell, K. M. Beardmore, S. Morris, and A. F. Tasch. An electric stopping power model for Monte Carlo and molecular dynamics simulation of ion implantation into silicon. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/276925.

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Spielauer, Martin, and René Houle. Sample size and statistical significance of hazard regression parameters. An exploration by means of Monte Carlo simulation of four transition models based on Hungarian GGS data. Rostock: Max Planck Institute for Demographic Research, June 2004. http://dx.doi.org/10.4054/mpidr-wp-2004-020.

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Rojas-Bernal, Alejandro, and Mauricio Villamizar-Villegas. Pricing the exotic: Path-dependent American options with stochastic barriers. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/be.1156.

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We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate Ratchet, Asian, and Barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, we highlight that our estimation requires less than 1% of the computational time compared to other standard methods, such as Monte Carlo simulations.
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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi, and Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, January 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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Melby, Jeffrey, Thomas Massey, Fatima Diop, Himangshu Das, Norberto Nadal-Caraballo, Victor Gonzalez, Mary Bryant, et al. Coastal Texas Protection and Restoration Feasibility Study : Coastal Texas flood risk assessment : hydrodynamic response and beach morphology. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41051.

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The US Army Corps of Engineers, Galveston District, is executing the Coastal Texas Protection and Restoration Feasibility Study coastal storm risk management (CSRM) project for the region. The project is currently in the feasibility phase. The primary goal is to develop CSRM measures that maximize national net economic development benefits. This report documents the coastal storm water level and wave hazard, including sea level rise, for a variety of flood risk management alternatives. Four beach restoration alternatives for Galveston Island and Bolivar peninsula were evaluated. Suites of synthetic tropical and historical non-tropical storms were developed and modeled. The CSTORM coupled surge-and-wave modeling system was used to accurately characterize storm circulation, water level, and wave hazards using new model meshes developed from high-resolution land and sub-aqueous surveys for with- and without-project scenarios. Beach morphology stochastic response was modeled with a Monte Carlo life-cycle simulation approach using the CSHORE morphological evolution numerical model embedded in the StormSim stochastic modeling system. Morphological and hydrodynamic response were primarily characterized with probability distributions of the number of rehabilitations and overflow.
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Bracken, Jerome. Monte Carlo Layered Defense Model. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada175217.

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Glaser, R. Monte Carlo simulation of scenario probability distributions. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/632934.

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