Thematische Bibliographien / Monte Carlo Simulation Technique

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Auswahl der wissenschaftlichen Literatur zum Thema „Monte Carlo Simulation Technique“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 21. Februar 2023

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Zeitschriftenartikel zum Thema "Monte Carlo Simulation Technique"

JAKUMEIT, JÜRGEN. „COMPUTATIONAL ASPECTS OF THE LOCAL ITERATIVE MONTE CARLO TECHNIQUE“. International Journal of Modern Physics C 11, Nr. 04 (Juni 2000): 665–73. http://dx.doi.org/10.1142/s0129183100000584.

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Lately, the Local Iterative Monte Carlo technique was introduced for an efficient simulation of effects connected to sparsely populated regions in semiconductor devices like hot electron effects in silicon MOSFETs. This paper focuses on computational aspects of this new Monte Carlo technique, namely the reduction of the computation time by parallel computation and the reuse of drift information. The Local Iterative Monte Carlo technique combines short Monte Carlo particle flight simulations with an iteration process to a complete device simulation. The separation between short Monte Carlo simulations and the iteration process makes a simple parallelization strategy possible. The necessary data transfer is small and can be performed via the Network File System. An almost linear speed up could be achieved. Besides by parallelization, the computational expenses can be significantly reduced, when the results of the short Monte Carlo simulations are memorized in a drift table and used several times. A comparison between a bulk, a one-dimensional and the two-dimensional Local Iterative Monte Carlo simulation reveals that by using the drift information more than once, becomes increasingly efficient with increasing dimension of the simulation.

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Suzuki, SHO, NAOKI Takano und 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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SWENDSEN, ROBERT H., BRIAN DIGGS, JIAN-SHENG WANG, SHING-TE LI, CHRISTOPHER GENOVESE und JOSEPH B. KADANE. „TRANSITION MATRIX MONTE CARLO“. International Journal of Modern Physics C 10, Nr. 08 (Dezember 1999): 1563–69. http://dx.doi.org/10.1142/s0129183199001340.

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Although histogram methods have been extremely effective for analyzing data from Monte Carlo simulations, they do have certain limitations, including the range over which they are valid and the difficulties of combining data from independent simulations. In this paper, we describe a complementary approach to extracting information from Monte Carlo simulations that uses the matrix of transition probabilities. Combining the Transition Matrix with an N-fold way simulation technique produces an extremely flexible and efficient approach to rather general Monte Carlo simulations.

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Marjanovic, Srdjan, und Milovan Suvakov. „Monte Carlo simulation of positronium thermalization in gases“. Chemical Industry 64, Nr. 3 (2010): 177–81. http://dx.doi.org/10.2298/hemind091221025m.

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In this paper we present the results of Monte Carlo simulations of positronium (Ps) swarm thermalization in helium (He) and water vapour. We have investigated the temporal evolution of energy and spatial parameters of the swarm and their sensitivity to the shape of the cross-section and the initial energy distribution. Positron anihilation spectroscopy (PAS) and positron emission tomography (PET) are techniques that depend on anihilation of positronium in materials and tissue. The results obtained point that the Monte Carlo technique shows good agreement with experimental results and is capable of accurately describing the behaviour of Ps particles including the energy, particle lifetime and the moment and location of the anihilation.

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Nedjalkov, M., und P. Vitanov. „MONTE CARLO TECHNIQUE FOR SIMULATION OF HIGH ENERGY ELECTRONS“. COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 10, Nr. 4 (April 1991): 525–30. http://dx.doi.org/10.1108/eb051726.

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Juang, C. H., X. H. Huang und D. J. Elton. „Fuzzy information processing by the Monte Carlo simulation technique“. Civil Engineering Systems 8, Nr. 1 (März 1991): 19–25. http://dx.doi.org/10.1080/02630259108970602.

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Fauzi, N. F. M., N. N. R. Roslan und M. I. M. Ridzuan. „Low voltage reliability equivalent using monte-carlo simulation technique“. IOP Conference Series: Materials Science and Engineering 863 (13.06.2020): 012042. http://dx.doi.org/10.1088/1757-899x/863/1/012042.

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Kosina, H., M. Nedjalkov und S. Selberherr. „An event bias technique for Monte Carlo device simulation“. Mathematics and Computers in Simulation 62, Nr. 3-6 (März 2003): 367–75. http://dx.doi.org/10.1016/s0378-4754(02)00245-8.

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Letosa, J., M. Garcia-Gracia, J. M. Fornies-Marquina und J. M. Artacho. „Performance limits in TDR technique by Monte Carlo simulation“. IEEE Transactions on Magnetics 32, Nr. 3 (Mai 1996): 958–61. http://dx.doi.org/10.1109/20.497401.

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Sherniyozov, A. A., und Sh D. Payziyev. „Simulating optical processes: Monte Carlo photon tracing method“. «Узбекский физический журнал» 24, Nr. 3 (11.09.2022): 157–62. http://dx.doi.org/10.52304/.v24i3.357.

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In search of high accuracy and convenient implementation, computational optics uses several modeling techniques such as Maxwell’s equations-based methods, ray-tracing method, Fourier optics methods and many others. Each with its advantages, disadvantages, and applicability caveats. In this paper, we describe Monte Carlo photon tracing as a simulation technique for computational optics. The validity of the main assumptions in the simulation method is demonstrated with examples, and limitations of method’s applicability are discussed.

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Dissertationen zum Thema "Monte Carlo Simulation Technique"

Rangaraj, Dharanipathy. „Multicomponent aerosol dynamics : exploration of direct simulation Monte Carlo technique /“. free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144452.

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Jackson, Andrew N. „Structural phase behaviour via Monte Carlo techniques“. Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/4850.

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There are few reliable computational techniques applicable to the problem of structural phase behaviour. This is starkly emphasised by the fact that there are still a number of unanswered questions concerning the solid state of some of the simplest models of matter. To determine the phase behaviour of a given system we invoke the machinery of statistical physics, which identifies the equilibrium phase as that which minimises the free-energy. This type of problem can only be dealt with fully via numerical simulation, as any less direct approach will involve making some uncontrolled approximation. In particular, a numerical simulation can be used to evaluate the free-energy difference between two phases if the simulation is free to visit them both. However, it has proven very difficult to find an algorithm which is capable of efficiently exploring two different phases, particularly when one or both of them is a crystalline solid. This thesis builds on previous work (Physical Review Letters 79 p.3002), exploring a new Monte Carlo approach to this class of problem. This new simulation technique uses a global coordinate transformation to switch between two different crystalline structures. Generally, this `lattice switch' is found to be extremely unlikely to succeed in a normal Monte Carlo simulation. To overcome this, extended-sampling techniques are used to encourage the simulation to visit `gateway' microstates where the switch will be successful. After compensating for this bias in the sampling, the free-energy difference between the two structures can be evaluated directly from their relative probabilities. As concrete examples on which to base the research, the lattice-switch Monte Carlo method is used to determine the free-energy difference between the face-centred cubic (fcc) and hexagonal close-packed (hcp) phases of two generic model systems --- the hard-sphere and Lennard-Jones potentials. The structural phase behaviour of the hard-sphere solid is determined at densities near melting and in the close-packed limit. The factors controlling the efficiency of the lattice-switch approach are explored, as is the character of the `gateway' microstates. The face-centred cubic structure is identified as the thermodynamically stable phase, and the free-energy difference between the two structures is determined with high precision. These results are shown to be in complete agreement with the results of other authors in the field (published during the course of this work), some of whom adopted the lattice-switch method for their calculations. Also, the results are favourably compared against the experimentally observed structural phase behaviour of sterically-stabilised colloidal dispersions, which are believed to behave like systems of hard spheres. The logical extension of the hard sphere work is to generalise the lattice-switch technique to deal with `softer' systems, such as the Lennard-Jones solid. The results in the literature for the structural phase behaviour of this relatively simple system are found to be completely inconsistent. A number of different approaches to this problem are explored, leading to the conclusion that these inconsistencies arise from the way in which the potential is truncated. Using results for the ground-state energies and from the harmonic approximation, we develop a new truncation scheme which allows this system to be simulated accurately and efficiently. Lattice-switch Monte Carlo is then used to determine the fcc-hcp phase boundary of the Lennard-Jones solid in its entirety. These results are compared against the experimental results for the Lennard-Jones potential's closest physical analogue, the rare-gas solids. While some of the published rare-gas observations are in approximate agreement with the lattice-switch results, these findings contradict the widely held belief that fcc is the equilibrium structure of the heavier rare-gas solids for all pressures and temperatures. The possible reasons for this disagreement are discussed. Finally, we examine the pros and cons of the lattice-switch technique, and explore ways in which it can be extended to cover an even wider range of structures and interactions.

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Can, Mutan Oya. „Comparison Of Regression Techniques Via Monte Carlo Simulation“. Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.

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The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares, least trimmed squares (LTS), Theil and weighted Theil are compared via computer simulation. In order to evaluate the estimator performance, mean, variance, bias, mean square error (MSE) and relative mean square error (RMSE) are computed.

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Louvin, Henri. „Development of an adaptive variance reduction technique for Monte Carlo particle transport“. Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS351/document.

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L’algorithme Adaptive Multilevel Splitting (AMS) a récemment fait son apparition dans la littérature de mathématiques appliquées, en tant que méthode de réduction de variance pour la simulation Monte Carlo de chaı̂nes de Markov. Ce travail de thèse se propose d’implémenter cette méthode de réduction de variance adaptative dans le code Monte-Carlo de transport de particules TRIPOLI-4,dédié entre autres aux études de radioprotection et d’instrumentation nucléaire. Caractérisées par de fortes atténuations des rayonnements dans la matière, ces études entrent dans la problématique du traitement d’évènements rares. Outre son implémentation inédite dans ce domaine d’application, deux nouvelles fonctionnalités ont été développées pour l’AMS, testées puis validées. La première est une procédure d’encaissement au vol permettant d’optimiser plusieurs scores en une seule simulation AMS. La seconde est une extension de l’AMS aux processus branchants, courants dans les simulations de radioprotection, par exemple lors du transport couplé de neutrons et des photons induits par ces derniers. L’efficacité et la robustesse de l’AMS dans ce nouveau cadre applicatif ont été démontrées dans des configurations physiquement très sévères (atténuations du flux de particules de plus de 10 ordres de grandeur), mettant ainsi en évidence les avantages prometteurs de l’AMS par rapport aux méthodes de réduction de variance existantes
The Adaptive Multilevel Splitting algorithm (AMS) has recently been introduced to the field of applied mathematics as a variance reduction scheme for Monte Carlo Markov chains simulation. This Ph.D. work intends to implement this adaptative variance reduction method in the particle transport Monte Carlo code TRIPOLI-4, dedicated among others to radiation shielding and nuclear instrumentation studies. Those studies are characterized by strong radiation attenuation in matter, so that they fall within the scope of rare events analysis. In addition to its unprecedented implementation in the field of particle transport, two new features were developed for the AMS. The first is an on-the-fly scoring procedure, designed to optimize the estimation of multiple scores in a single AMS simulation. The second is an extension of the AMS to branching processes, which are common in radiation shielding simulations. For example, in coupled neutron-photon simulations, the neutrons have to be transported alongside the photons they produce. The efficiency and robustness of AMS in this new framework have been demonstrated in physically challenging configurations (particle flux attenuations larger than 10 orders of magnitude), which highlights the promising advantages of the AMS algorithm over existing variance reduction techniques

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Nilsson, Emma. „Monte Carlo simulation techniques : The development of a general framework“. Thesis, Linköping University, Department of Management and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18327.

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Algorithmica Research AB develops software application for the financial markets. One of their products is Quantlab that is a tool for quantitative analyses. An effective method to value several financial instruments is Monte Carlo simulation. Since it is a common method Algorithmica is interesting in investigating if it is possible to create a Monte Carlo framework.

A requirement from Algorithmica is that the framework is general and this is the main problem to solve. It is difficult to generate a generalized framework because financial derivatives have very different appearances. To simplify the framework the thesis will be delimitated to European style derivatives where the underlying asset is following a Geometric Brownian Motion.

The definition of the problem and delimitation were defined gradually, in parallel with the review of literature, this to be able to decide what purpose, and delimitations that is reasonable to treat. Standard Monte Carlo requires a large number of trials and is therefore slow. To speed up the process there exist different variance reduction techniques and also Quasi Monte Carlo simulation, where deterministic numbers (low discrepancy sequences) is used instead of random. The thesis investigated the variance reduction techniques; control variate technique, antithetic variate technique, and the low discrepancy sequences; Sobol, Faure and Halton.

Three test instruments were chosen to test the framework, an Asian option and a Barrier option where the purpose is to conclude which Monte Carle method that performs best, and also a structured product; Smart Start, that is more complex and the purpose is to test that the framework can handle it.

To increase the understanding of the theory the Halton, Faure and Sobol sequence were implemented in Quantlab in parallel with the review of literature. The Halton and Faure sequences also seemed to perform worse than Sobol so they were not further analyzed.

The developing of the framework was an iterative process. The chosen solution is to design a general framework by using five function pointers; the path generator, the payoff function, the stop criterion function and the volatility and interest rates. The user specifies these functions by him/her given some obligatory input and output values. It is not a problem-free solution to use function pointers and several conflicts and issues are defined, therefore it is not recommended to implement the framework as it is designed today.

In parallel with the developing of the framework several experiments on the Asian and Barrier options were performed with varying result and it is not possible to draw a conclusion on which method that is best. Often Sobol seems to converge better and fluctuates less than standard Monte Carlo. The literature indicates that it is important that the user has an understanding of the instrument that should be valued, the stochastic process it follows and the advantages and disadvantages of different Monte Carlo methods. It is recommended to evaluate the different method with experiments, before deciding which method to use when valuing a new derivative.

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Ahmad, Abdul Ossman. „Advances in an open-source direct simulation Monte Carlo technique for hypersonic rarefied gas flows“. Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=26579.

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Hypersonic vehicles that travel through rarefied gas environments are very expensive to design through experimental methods. In the last few decades major work has been carried out in developing numerical methods to capture these types of flows to a certain degree of accuracy. This accuracy is increased using particle based numerical techniques as opposed to continuum computational fluid dynamics. However, one of the modern problems of particle based techniques is the high computational cost associated with it. This thesis presents an enhanced open-source particle based technique to capture high speed rarefied gas flows. This particle based technique is called dsmcFoam and is based on the direct simulation Monte Carlo technique. As a result of the author's work dsmcFoam has become more efficient and accurate. Benchmark studies of the standard dsmcFoam solver will be presented before introducing the main advances. The results of the benchmark investigations are compared with analytical solutions, other DSMC codes and experimental data available in the literature. And excellent agreement is found when good DSMC practice has been followed. The main advances of dsmcFoam discussed are a routine for selecting collision pairs called the transient adaptive sub-cell (TASC) method and a dynamic wall temperature model (DWTM). The DWTM relates the wall temperature to the heat flux. In addition, verification and validation studies are undertaken of the DWTM. Furthermore, the widely used conventional 8 sub-cell method used to select possible collision pairs becomes very cumbersome to employ properly. This is because many mesh refinement stages are required in order to obtain accurate data. Instead of mesh refinement the TASC technique automatically employs more sub-cells, and these sub-cells are based on the number of particles in a cell. Finally, parallel efficiency tests of dsmcFoam are presented in this thesis along with a new domain decomposition technique for parallel processing. This technique splits up the computational domain based on the number of particles, such that each processor has the same number of particles to work with.

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Lester, Christopher. „Efficient simulation techniques for biochemical reaction networks“. Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:bb804e01-b1de-409f-b843-4806c2c990c2.

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Discrete-state, continuous-time Markov models are becoming commonplace in the modelling of biochemical processes. The mathematical formulations that such models lead to are opaque, and, due to their complexity, are often considered analytically intractable. As such, a variety of Monte Carlo simulation algorithms have been developed to explore model dynamics empirically. Whilst well-known methods, such as the Gillespie Algorithm, can be implemented to investigate a given model, the computational demands of traditional simulation techniques remain a significant barrier to modern research. In order to further develop and explore biologically relevant stochastic models, new and efficient computational methods are required. In this thesis, high-performance simulation algorithms are developed to estimate summary statistics that characterise a chosen reaction network. The algorithms make use of variance reduction techniques, which exploit statistical properties of the model dynamics, so that the statistics can be computed efficiently. The multi-level method is an example of a variance reduction technique. The method estimates summary statistics of well-mixed, spatially homogeneous models by using estimates from multiple ensembles of sample paths of different accuracies. In this thesis, the multi-level method is developed in three directions: firstly, a nuanced implementation framework is described; secondly, a reformulated method is applied to stiff reaction systems; and, finally, different approaches to variance reduction are implemented and compared. The variance reduction methods that underpin the multi-level method are then re-purposed to understand how the dynamics of a spatially-extended Markov model are affected by changes in its input parameters. By exploiting the inherent dynamics of spatially-extended models, an efficient finite difference scheme is used to estimate parametric sensitivities robustly. The new simulation methods are tested for functionality and efficiency with a range of illustrative examples. The thesis concludes with a discussion of our findings, and a number of future research directions are proposed.

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Baker, Adam Richard Ernest. „The use of the Monte Carlo technique in the simulation of small-scale dosimeters and microdosimeters“. Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/2897/.

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In order to understand the effects of low keV radiation upon small scales, a number of detector designs have been developed to investigate the ways energy is deposited. This research was conducted in order to investigate a number of different detector designs, looking in particular at their properties as small scale dosimeters exposed to photon radiation with an energy of 5-50 keV. In addition to this, Monte Carlo models were constructed of the different detector designs in order to ascertain the trends in energy absorption within the detectors. An important part of the research was investigating the dose enhancement effects produced when the low Z elements present in human tissues are in proximity to higher Z metallic elements within this energy range. This included looking at dose enhancement due to the photoelectric effect, with a photon energy of 5-50 keV and through the absorption of thermal neutrons. The reason for studying the dose enhancement was twofold - looking at the increase in energy absorption for elements that are currently being investigated for medical applications as well as elements that are present in dosemeters alongside the tissue equivalent elements. By comparing the results produced using the Monte Carlo codes MCNP4C and EGSnrc, simulations were produced for a variety of different detector designs, both solid state and gasfilled. These models were then compared with experimental results and were found to be able to predict trends in the behaviour of some of the detector designs.

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Mastail, Cédric. „Modélisation et simulation du dépôt des oxydes à forte permittivité par la technique du Monte-Carlo cinétique“. Toulouse 3, 2009. http://thesesups.ups-tlse.fr/989/.

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Miniaturiser les composants impose des changements radicaux pour l'élaboration des dispositifs micro électroniques du futur. Dans ce cadre, les oxydes de grille MOS atteignent des épaisseurs limites qui les rendent perméables aux courants de fuite. Une solution est de remplacer le SiO2 par un matériau de permittivité plus élevée permettant l'utilisation de couches plus épaisses pour des performances comparables. Dans ce travail nous présentons une modélisation multi-échelle de la croissance par couche atomique (ALD) d'HfO2 sur Si permettant de relier la nano-structuration d'une interface au procédé d'élaboration. Nous montrons que la connaissance de processus chimiques élémentaires, via des calculs DFT, permet d'envisager une simulation procédé qui repose sur le développement d'un logiciel de type Monte Carlo Cinétique nommé "HIKAD". Au delà des mécanismes les plus évidents, adsorption, désorption, décomposition et hydrolyse des précurseurs sur la surface, nous introduirons la notion de mécanismes de densification des couches d'oxyde déposées. Ces mécanismes sont l'élément clé permettant de comprendre comment s'effectue la croissance de la couche en termes de couverture. Mais au delà de cet aspect ils nous permettent d'appréhender comment, à partir de réactions de type moléculaire le système évolue vers un matériau massif. Nous discuterons ces divers éléments à la lumière de résultats de caractérisations obtenus récemment sur le plan expérimental du dépôt d'oxydes d'hafnium
Miniaturizing components requires radical changes in the development of future micro electronic devices. In this perspective, the gate dielectric of MOS devices can become so thin as to be made permeable to leakage currents. One solution is to replace SiO2 by a material with a higher permittivity which would allow the use of thicker layers with similar results. My work presents a multi-scale modelling of the growth of HfO2 on Si by atomic layer (ALD), which allows me to link the nano-structuration of an interface with the process of development. I demonstrate that knowing how basic chemical processes work, thanks to DFT calculations, allows considering a process simulation based on the development of a Kinetic Monte Carlo software named "HIKAD. " Going beyond rather obvious mechanisms, I introduce the notion of densification mechanisms of deposited oxide layers. These mechanisms are the key element to understand how the growth of the layer in terms of coverage works. But even beyond that aspect, they allow to study the system's evolution towards a massive material, starting from molecular reactions. I shall discuss all those points in the light of recent experimental characterisation results concerning the deposition of hafnium oxides

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Mastail, Cedric. „Modélisation et simulation du dépôt des oxydes à forte permittivité par la technique du Monte-Carlo cinétique“. Phd thesis, Université Paul Sabatier - Toulouse III, 2009. http://tel.archives-ouvertes.fr/tel-00541993.

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Bücher zum Thema "Monte Carlo Simulation Technique"

Mun, Johnathan. Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. 2. Aufl. New York: Wiley, 2010.

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Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. Hoboken, NJ: John Wiley & Sons, 2006.

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Mooney, Christopher. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.

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Monte Carlo simulation. Thousand Oaks, Calif: Sage Publications, 1997.

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Zhu, Zhen, und Hari Rajagopalan. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.

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Hess, Karl, Hrsg. Monte Carlo Device Simulation. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7.

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Thomopoulos, Nick T. Essentials of Monte Carlo Simulation. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6022-0.

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Brandimarte, Paolo. Handbook in Monte Carlo Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593264.

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Gleißner, Werner, und Marco Wolfrum. Risikoaggregation und Monte-Carlo-Simulation. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24274-9.

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Pierre, L' Ecuyer, und Owen Art B, Hrsg. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.

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Buchteile zum Thema "Monte Carlo Simulation Technique"

Frenkel, D. „Advanced Monte Carlo Techniques“. In Computer Simulation in Chemical Physics, 93–152. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1679-4_4.

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Woolard, D. L., H. Tian, M. A. Littlejohn, R. J. Trew und 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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Demirtas, Hakan. „A Multiple Imputation Framework for Massive Multivariate Data of Different Variable Types: A Monte-Carlo Technique“. In Monte-Carlo Simulation-Based Statistical Modeling, 143–62. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_8.

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Sarrut, D., und N. Krah. „Artificial Intelligence and Monte Carlo Simulation“. In Monte Carlo Techniques in Radiation Therapy, 251–58. 2. Aufl. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003211846-20.

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Hanagaki, Kazunori, Junichi Tanaka, Makoto Tomoto und Yuji Yamazaki. „Event Simulation“. In Experimental Techniques in Modern High-Energy Physics, 115–23. Tokyo: Springer Japan, 2022. http://dx.doi.org/10.1007/978-4-431-56931-2_7.

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AbstractNot only real experimental data but also simulated data are necessary for modern data analyses because experiments, that is, detectors, etc. are getting complex so that we need the help of Monte Carlo (MC) simulation to understand the experimental data.

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L'Ecuyer, Pierre, François Le Gland, Pascal Lezaud und Bruno Tuffin. „Splitting Techniques“. In Rare Event Simulation using Monte Carlo Methods, 39–61. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.ch3.

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Thomson, Rowan M., Åsa Carlsson Tedgren, Gabriel Fonseca, Guillaume Landry, Brigitte Reniers, Mark J. Rivard, Jeffrey F. Williamson und Frank Verhaegen. „Monte Carlo Simulation in Brachytherapy Patients and Applicator Modelling“. In Monte Carlo Techniques in Radiation Therapy, 235–49. 2. Aufl. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003211846-19.

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Shigeta, K., K. Tanaka, T. Iizuka, H. Kato und H. Matsumoto. „A New Statistical Enhancement Technique in Parallelized Monte Carlo Device Simulation“. In Simulation of Semiconductor Devices and Processes, 384–87. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6619-2_93.

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Siwach, Vikash, Manju S. Tonk und Hemant Poonia. „Simulation of Queues in Sugar Mills Using Monte Carlo Technique“. In Springer Proceedings in Mathematics & Statistics, 481–93. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7272-0_33.

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Allen, Theodore T. „Variance Reduction Techniques and Quasi-Monte Carlo“. In Introduction to Discrete Event Simulation and Agent-based Modeling, 111–24. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-139-4_8.

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Konferenzberichte zum Thema "Monte Carlo Simulation Technique"

Graham, G. E. „DZero Monte Carlo simulation“. In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405336.

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Baoxin Li, R. Chellappa und Hankyu Moon. „Monte Carlo simulation techniques for probabilistic tracking“. In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.986883.

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Amini, Abolfazl M., George E. Ioup und Juliette W. Ioup. „Metropolis Monte Carlo deconvolution technique compared to iterative methods for noisy data“. In Aerospace/Defense Sensing, Simulation, and Controls, herausgegeben von Ivan Kadar. SPIE, 2001. http://dx.doi.org/10.1117/12.436983.

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Abu Husain, M. K., N. I. Mohd Zaki, M. B. Johari und G. Najafian. „Extreme Response Prediction for Fixed Offshore Structures by Monte Carlo Time Simulation Technique“. In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54200.

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For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structure is essential for safe and economical design of these structures. Many different techniques have been introduced for evaluation of statistical properties of response. In each case, sea-states are characterised by an appropriate water surface elevation spectrum, covering a wide range of frequencies. In reality, the most versatile and reliable technique for predicting the statistical properties of the response of an offshore structure to random wave loading is the time domain simulation technique. To this end, conventional time simulation (CTS) procedure or commonly called Monte Carlo time simulation method is the best known technique for predicting the short-term and long-term statistical properties of the response of an offshore structure to random wave loading due to its capability of accounting for various nonlinearities. However, this technique requires very long simulations in order to reduce the sampling variability to acceptable levels. In this paper, the effect of sampling variability of a Monte Carlo technique is investigated.

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Drosg, M., Marianne E. Hamm und Robert W. Hamm. „Complete Monte Carlo Simulation of Neutron Scattering Experiments“. In APPLICATIONS OF NUCLEAR TECHNIQUES: Eleventh International Conference. AIP, 2011. http://dx.doi.org/10.1063/1.3665300.

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BOWLES, ROLAND, TONY LAITURI und GEORGE TREVINO. „A Monte Carlo simulation technique for low-altitude, wind-shear turbulence“. In 28th Aerospace Sciences Meeting. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-564.

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Sadowsky, J. S., und J. A. Bucklew. „Large deviations theory techniques in Monte Carlo simulation“. In the 21st conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76738.76804.

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Perry, Vincent, Wendy Gao, Michael Chen, J. Michael Barton und Simon Su. „Visualization Techniques for Large-Scale Monte Carlo Simulation“. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006423.

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Irimia, Daniel, und Jens O. M. Karlsson. „Monte Carlo Simulation of Ice Formation in Tissues“. In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32681.

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Freezing is a common technique for preservation of isolated cells, and extending its applications to the preservation of tissues would have important implications for the storage and distribution of tissue engineered products. Unlike isolated cells in suspension, cells in tissue interact with each other, and this interaction is known to affect the outcome of tissue cryopreservation. As a consequence, our knowledge of the cryobiology of isolated cells cannot simply be extrapolated to tissues, and new models, which consider the interaction between cells, need to be developed. The model that we propose is based on previous quantitative analysis of intercellular ice propagation in a micropatterned two-cell system. We used Monte Carlo simulations to extrapolate the results from cell pairs to two-dimensional and three-dimensional tissues. Effects of tissue geometry, cellular connectivity, and degree of intercellular interaction were investigated.

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Ren, Xiao-ping, Yun Yang, Fang Nan, Zi-yu Li, Jing-ru Chi und Fan Yang. „An Indicator Determination Technique based on Delphi Approach and Monte Carlo Simulation“. In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833173.

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Berichte der Organisationen zum Thema "Monte Carlo Simulation Technique"

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

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Xu, S. L., B. Lai und P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), Februar 1992. http://dx.doi.org/10.2172/10134610.

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Douglas, L. J. Monte Carlo Simulation as a Research Management Tool. Office of Scientific and Technical Information (OSTI), Juni 1986. http://dx.doi.org/10.2172/1129252.

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Aguayo Navarrete, Estanislao, Austin S. Ankney, Timothy J. Berguson, Richard T. Kouzes, John L. Orrell, Meredith D. Troy und Clinton G. Wiseman. Monte Carlo Simulation Tool Installation and Operation Guide. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095434.

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Boyd, Lain D. Monte Carlo Simulation of Radiation in Hypersonic Flows. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada414031.

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Clegg, Benjamin Wyatt, David H. Collins, Jr. und 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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Rensink, M. E., und T. D. Rognlien. Progress on coupling UEDGE and Monte-Carlo simulation codes. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/380308.

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Martin, William R., James Paul Holloway, Kaushik Banerjee, Jesse Cheatham und Jeremy Conlin. Global Monte Carlo Simulation with High Order Polynomial Expansions. Office of Scientific and Technical Information (OSTI), Dezember 2007. http://dx.doi.org/10.2172/920974.

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Holliday, Mary R. Methodology of an Event-Driven Monte Carlo Missile Simulation. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada601300.

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