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Добірка наукової літератури з теми "Kinetic Monte Carlo Methods"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Kinetic Monte Carlo Methods"

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Srivastava, Argala, K. P. Singh, and S. B. Degweker. "Monte Carlo Methods for Reactor Kinetic Simulations." Nuclear Science and Engineering 189, no. 2 (November 14, 2017): 152–70. http://dx.doi.org/10.1080/00295639.2017.1388091.

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2

Herty, M., A. Klar, and L. Pareschi. "General Kinetic Models for Vehicular Traffic Flows and Monte-Carlo Methods." Computational Methods in Applied Mathematics 5, no. 2 (2005): 155–69. http://dx.doi.org/10.2478/cmam-2005-0008.

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AbstractIn this paper we present a general derivation of kinetic models for traffic flows including different kinds of interaction rules. We show that most kinetic previously derived models can be cast in the actual formulation. The development of Monte-Carlo methods for direct simulation of kinetic models is considered as an initial step towards realistic and effcient computations of traffic phenomena. Monte-Carlo methods are developed for these kinetic models. Several numerical examples are computed and compared to the previously obtained solutions of the stationary equation.
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3

Xiaopeng Xu, Xiaopeng Xu, Chuancai Liu Xiaopeng Xu, Hongji Yang Chuancai Liu, and Xiaochun Zhang Hongji Yang. "A Multi-Trajectory Monte Carlo Sampler." 網際網路技術學刊 23, no. 5 (September 2022): 1117–28. http://dx.doi.org/10.53106/160792642022092305020.

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<p>Markov Chain Monte Carlo techniques based on Hamiltonian dynamics can sample the first or last principal components of multivariate probability models using simulated trajectories. However, when components&rsquo; scales span orders of magnitude, these approaches may be unable of accessing all components adequately. While it is possible to reconcile the first and last components by alternating between two different types of trajectories, the sampling of intermediate components may be imprecise. In this paper, a function generalizing the kinetic energies of Hamiltonian Monte Carlo and Riemannian Manifold Hamiltonian Monte Carlo is proposed, and it is found that the methods based on a specific form of the function can more accurately sample normal distributions. Additionally, the multi-particle algorithm&rsquo;s reasoning is given after a review of some statistical ideas.</p> <p>&nbsp;</p>
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4

Takano, Hiroshi. "On Monte Carlo Methods for the Kinetic Ising Model." Journal of the Physical Society of Japan 62, no. 1 (January 15, 1993): 370–71. http://dx.doi.org/10.1143/jpsj.62.370.

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5

Khrushcheva, O., E. E. Zhurkin, L. Malerba, C. S. Becquart, C. Domain, and M. Hou. "Copper precipitation in iron: a comparison between metropolis Monte Carlo and lattice kinetic Monte Carlo methods." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 202 (April 2003): 68–75. http://dx.doi.org/10.1016/s0168-583x(02)01830-x.

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6

Kozubski, Rafal, Graeme E. Murch, and Irina V. Belova. "Vacancy-Mediated Diffusion and Diffusion-Controlled Processes in Ordered Binary Intermetallics by Kinetic Monte Carlo Simulations." Diffusion Foundations 29 (April 2021): 95–115. http://dx.doi.org/10.4028/www.scientific.net/df.29.95.

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We review the results of our Monte Carlo simulation studies carried out within the past two decades in the area of atomic-migration-controlled phenomena in intermetallic compounds. The review aims at showing the high potential of Monte Carlo methods in modelling both the equilibrium states of the systems and the kinetics of the running processes. We focus on three particular problems: (i) the atomistic origin of the complexity of the ‘order-order’ relaxations in γ’-Ni3Al; (ii) surface-induced ordering phenomena in γ-FePt and (iii) ‘order—order’ kinetics and self-diffusion in the ‘triple-defect’ β-NiAl. The latter investigation demonstrated how diverse Monte Carlo techniques may be used to model the phenomena where equilibrium thermodynamics interplays and competes with kinetic effects.
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Kaiser, Waldemar, Manuel Gößwein, and Alessio Gagliardi. "Acceleration scheme for particle transport in kinetic Monte Carlo methods." Journal of Chemical Physics 152, no. 17 (May 7, 2020): 174106. http://dx.doi.org/10.1063/5.0002289.

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Carrillo, José Antonio, and Mattia Zanella. "Monte Carlo gPC Methods for Diffusive Kinetic Flocking Models with Uncertainties." Vietnam Journal of Mathematics 47, no. 4 (November 5, 2019): 931–54. http://dx.doi.org/10.1007/s10013-019-00374-2.

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Abstract In this paper we introduce and discuss numerical schemes for the approximation of kinetic equations for flocking behavior with phase transitions that incorporate uncertain quantities. This class of schemes here considered make use of a Monte Carlo approach in the phase space coupled with a stochastic Galerkin expansion in the random space. The proposed methods naturally preserve the positivity of the statistical moments of the solution and are capable to achieve high accuracy in the random space. Several tests on a kinetic alignment model with self propulsion validate the proposed methods both in the homogeneous and inhomogeneous setting, shading light on the influence of uncertainties in phase transition phenomena driven by noise such as their smoothing and confidence band.
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Koblents, Eugenia, Inés P. Mariño, and Joaquín Míguez. "Bayesian Computation Methods for Inference in Stochastic Kinetic Models." Complexity 2019 (January 20, 2019): 1–15. http://dx.doi.org/10.1155/2019/7160934.

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In this paper we investigate Monte Carlo methods for the approximation of the posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump processes that model the interactions among species in biological systems according to a set of usually unknown parameters. The tracking of the species populations together with the estimation of the interaction parameters is a Bayesian inference problem for which Markov chain Monte Carlo (MCMC) methods have been a typical computational tool. Specifically, the particle MCMC (pMCMC) method has been shown to be effective, while computationally demanding method applicable to this problem. Recently, it has been shown that an alternative approach to Bayesian computation, namely, the class of adaptive importance samplers, may be more efficient than classical MCMC-like schemes, at least for certain applications. For example, the nonlinear population Monte Carlo (NPMC) algorithm has yielded promising results with a low dimensional SKM (the classical predator-prey model). In this paper we explore the application of both pMCMC and NPMC to analyze complex autoregulatory feedback networks modelled by SKMs. We demonstrate numerically how the populations of the relevant species in the network can be tracked and their interaction rates estimated, even in scenarios with partial observations. NPMC schemes attain an appealing trade-off between accuracy and computational cost that can make them advantageous in many practical applications.
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Hehr, Brian D. "Analysis of Radiation Effects in Silicon Using Kinetic Monte Carlo Methods." IEEE Transactions on Nuclear Science 61, no. 6 (December 2014): 2847–54. http://dx.doi.org/10.1109/tns.2014.2368075.

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Дисертації з теми "Kinetic Monte Carlo Methods"

1

Mandreoli, Lorenzo. "Density based Kinetic Monte Carlo Methods." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975329111.

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Höök, Lars Josef. "Variance reduction methods for numerical solution of plasma kinetic diffusion." Licentiate thesis, KTH, Fusionsplasmafysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91332.

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Performing detailed simulations of plasma kinetic diffusion is a challenging task and currently requires the largest computational facilities in the world. The reason for this is that, the physics in a confined heated plasma occur on a broad range of temporal and spatial scales. It is therefore of interest to improve the computational algorithms together with the development of more powerful computational resources. Kinetic diffusion processes in plasmas are commonly simulated with the Monte Carlo method, where a discrete set of particles are sampled from a distribution function and advanced in a Lagrangian frame according to a set of stochastic differential equations. The Monte Carlo method introduces computational error in the form of statistical random noise produced by a finite number of particles (or markers) N and the error scales as αN−β where β = 1/2 for the standard Monte Carlo method. This requires a large number of simulated particles in order to obtain a sufficiently low numerical noise level. Therefore it is essential to use techniques that reduce the numerical noise. Such methods are commonly called variance reduction methods. In this thesis, we have developed new variance reduction methods with application to plasma kinetic diffusion. The methods are suitable for simulation of RF-heating and transport, but are not limited to these types of problems. We have derived a novel variance reduction method that minimizes the number of required particles from an optimization model. This implicitly reduces the variance when calculating the expected value of the distribution, since for a fixed error the  optimization model ensures that a minimal number of particles are needed. Techniques that reduce the noise by improving the order of convergence, have also been considered. Two different methods have been tested on a neutral beam injection scenario. The methods are the scrambled Brownian bridge method and a method here called the sorting and mixing method of L´ecot and Khettabi[1999]. Both methods converge faster than the standard Monte Carlo method for modest number of time steps, but fail to converge correctly for large number of time steps, a range required for detailed plasma kinetic simulations. Different techniques are discussed that have the potential of improving the convergence to this range of time steps.
QC 20120314
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3

Herron, Adam David. "Mesoscale Modeling of Shape Memory Alloys by Kinetic Monte Carlo–Finite Element Analysis Methods." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8261.

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A coupled kinetic Monte Carlo – Finite Element Analysis (kMC–FEA) method is developed with a numerical implementation in the Scalable Implementation of Finite Elements at NASA (ScIFEN). This method is presented as a mesoscale model for Shape Memory Alloy (SMA) material systems. The model is based on Transition State Theory and predicts the nonlinear mechanical behavior of the 1st order solid–solid phase transformation between Austenite and Martensite in SMAs. The kMC–FEA modeling method presented in this work builds upon the work of Chen and Schuh [1, 2]. It represents a “bottom-up” approach to materials modeling and could serve as a bridge for future studies that attempt to link ab initio methods with phenomenological findings in SMA systems. This thesis presents the derivation of the kMC–FEA model, which is then used to probe the various responses expected in SMAs and verify the influence of model parameters on simulation behavior. In a departure from the work of Chen and Schuh, the thermodynamic derivation includes an elastic transformation energy term, which is found to be a significant fraction of the total transformation energy and play an important role in the evolution of a simulation. Theoretical predictions of the model behavior can be made from this derivation, including expected transformation stresses and temperatures. A convergence study is presented as verification that the new elastic energy term proposed in this model is a reasonable approximation. A parameter sensitivity study is also presented, showing good agreement between theoretical predictions and the results of a full-factorial numerical exploration of model outputs. Model simulation demonstrates the emergence of the shape memory effect, an important SMA behavior not shown by Chen and Schuh, along with the expected superelastic effect and thermal hysteresis. Further exploration of simulated model outputs presented in this work involves comparison with experimental data and predicted output values obtained from a separate phenomenological constitutive model. This comparison shows that the kMC–FEA method is capable of reproducing qualitative, but not yet quantitative, responses of real SMA material systems. Discussion of each model parameter and its effects on the behavior of the model are presented as guidelines for future studies of SMA materials. A complete implementation of the method is contained in a new finite element software package (ScIFEN) that is available for future
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4

Schmidt, Daniel. "Kinetic Monte Carlo Methods for Computing First Capture Time Distributions in Models of Diffusive Absorption." Scholarship @ Claremont, 2017. https://scholarship.claremont.edu/hmc_theses/97.

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In this paper, we consider the capture dynamics of a particle undergoing a random walk above a sheet of absorbing traps. In particular, we seek to characterize the distribution in time from when the particle is released to when it is absorbed. This problem is motivated by the study of lymphocytes in the human blood stream; for a particle near the surface of a lymphocyte, how long will it take for the particle to be captured? We model this problem as a diffusive process with a mixture of reflecting and absorbing boundary conditions. The model is analyzed from two approaches. The first is a numerical simulation using a Kinetic Monte Carlo (KMC) method that exploits exact solutions to accelerate a particle-based simulation of the capture time. A notable advantage of KMC is that run time is independent of how far from the traps one begins. We compare our results to the second approach, which is asymptotic approximations of the FPT distribution for particles that start far from the traps. Our goal is to validate the efficacy of homogenizing the surface boundary conditions, replacing the reflecting (Neumann) and absorbing (Dirichlet) boundary conditions with a mixed (Robin) boundary condition.
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5

Gong, Min. "A study of surface growth mechanism by kinetic Monte-Carlo simulation." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37636194.

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Gong, Min, and 鞏旻. "A study of surface growth mechanism by kinetic Monte-Carlo simulation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37636194.

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Alexander, Kathleen Carmody. "An off-lattice kinetic Monte Carlo method for the investigation of grain boundary kinetic processes." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/108218.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2016.
"September 2016." Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 155-171).
Kinetic Monte Carlo (Kc) methods have the potential to extend the accessible timescales of off-lattice atomistic simulations beyond the limits of molecular dynamics by making use of transition state theory and parallelization. However, it is a challenge to identify a complete catalog of events accessible to an off-lattice system in order to accurately calculate the residence time for Kc. Possible approaches to some of the key steps needed to address this problem are developed in this thesis. After validating these methods in the study of vacancy diffusion, we implemented our off-lattice Kc method to study the kinetic behavior of the [Sigma]5 (210) grain boundary (GB) in copper. We found that the activation energy associated with intrinsic diffusion at this GB is between the activation energies of interstitial diffusion and vacancy diffusion. We have also measured GB mobility in this system and found the activation energy of GB migration to be similar to that of bulk diffusion. For comparison, we have performed a molecular dynamics study of this target GB and obtained diffusivity and mobility estimates that are sufficiently similar to our Kc results at high temperatures. At low temperatures, the molecular dynamics simulations did not yield meaningful predictions. The results of this case study indicate that the off-lattice Kc method developed herein may provide a means to study GB kinetic properties under conditions and timescales that were previously inaccessible. Towards the end of developing predictive relationships to describe GB kinetic properties, we have begun to assess whether the normalized ground state residence time of a GB is a good predictor of kinetic behavior by analyzing several low-CSL GBs. We see a clear relationship between normalized ground state residence time and kinetic properties for the GBs considered so far. A more thorough investigation will be required to establish whether or not these preliminary findings indicate a more general relationship.
by Kathleen Carmody Alexander.
Ph. D.
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8

Hay, Aaron M. "Applying massively parallel kinetic Monte Carlo methods to simulate grain growth and sintering in powdered metals." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5583.

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Approved for public release; distribution is unlimited.
50 nm) can be used to bond materials at dramatically lower temperatures and pressures while maintaining the mechanical properties of nanostructured materials. Despite these promising results, the grain growth and sintering mechanisms of nanostructures are not fully understood. Simulations performed using KMC algorithms can be used to model nanoparticle grain growth and sintering. Sandia National Laboratories' new, massively-parallel, Stochastic Parallel Particle Kinetic Simulator (SPPARKS) code is capable of simulating large-scale problems of grain growth and sintering from the nanoscale to the microscale. This thesis focused on setting up SPPARKS on the Naval Postgraduate School's high performance computing resources. The performance of SPPARKS was assessed for large-scale simulations of grain growth and sintering. Using SPPARKS, the ability to perform coupled grain growth and sintering was demonstrated while controlling variables such as temperature, porosity, and grain size. The results demonstrate the importance of the spatial distribution of porosity on the nanostructure evolution during grain growth and sintering.
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9

Shi, Feng. "Nucleation and growth in materials and on surfaces : kinetic Monte Carlo simulations and rate equation theory /." Connect to full text in OhioLINK ETD Center, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1216839589.

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Morris, Aaron Benjamin. "Investigation of a discrete velocity Monte Carlo Boltzmann equation." Thesis, [Austin, Tex. : University of Texas, 2009. http://hdl.handle.net/2152/ETD-UT-2009-05-127.

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Книги з теми "Kinetic Monte Carlo Methods"

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Center, Ames Research, ed. Particle kinetic simulation of high altitude hypervelocity flight. [Moffett Field, Calif.]: NASA National Aeronautics and Space Administration, Ames Research Center, 1994.

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2

L, Haas Brian, and United States. National Aeronautics and Space Administration., eds. Particle kinetic simulation of high altitude hypervelocity flight. [Washington, DC: National Aeronautics and Space Administration, 1994.

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3

Daw, Murray S. Atomic-scale modeling of the structure and dynamics of dislocations in complex alloys at high temperatures. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2003.

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4

Daw, Murray S. Atomic-scale modeling of the structure and dynamics of dislocations in complex alloys at high temperatures. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2003.

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5

Daw, Murray S. Atomic-scale modeling of the structure and dynamics of dislocations in complex alloys at high temperatures. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2003.

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6

L, Haas Brian, and United States. National Aeronautics and Space Administration., eds. Particle kinetic simulation of high altitude hypervelocity flight. [Washington, DC: National Aeronautics and Space Administration, 1994.

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7

Kalos, Malvin H. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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8

Chowdhury, Sujaul. Monte Carlo Methods. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02429-0.

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9

Barbu, Adrian, and Song-Chun Zhu. Monte Carlo Methods. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5.

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Kalos, Malvin H., and Paula A. Whitlock, eds. Monte Carlo Methods. Weinheim, Germany: Wiley-VCH Verlag GmbH, 1986. http://dx.doi.org/10.1002/9783527617395.

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Частини книг з теми "Kinetic Monte Carlo Methods"

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Bhushan, Bharat, and Manuel L. B. Palacio. "Kinetic Monte Carlo Method." In Encyclopedia of Nanotechnology, 1179. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-90-481-9751-4_100332.

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Trochet, Mickaël, Normand Mousseau, Laurent Karim Béland, and Graeme Henkelman. "Off-Lattice Kinetic Monte Carlo Methods." In Handbook of Materials Modeling, 715–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-44677-6_29.

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Trochet, Mickaël, Normand Mousseau, Laurent Karim Béland, and Graeme Henkelman. "Off-Lattice Kinetic Monte Carlo Methods." In Handbook of Materials Modeling, 1–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-42913-7_29-1.

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Trochet, Mickaël, Normand Mousseau, Laurent Karim Béland, and Graeme Henkelman. "Off-Lattice Kinetic Monte Carlo Methods." In Handbook of Materials Modeling, 1–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-42913-7_29-2.

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Rjasanow, Sergej. "Monte-Carlo methods for the Boltzmann equation." In Modeling and Computational Methods for Kinetic Equations, 81–115. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8200-2_3.

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Binder, K., and M. H. Kalos. "Monte Carlo Studies of Relaxation Phenomena: Kinetics of Phase Changes and Critical Slowing Down." In Monte Carlo Methods in Statistical Physics, 225–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82803-4_6.

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7

Gurov, Todor V., and Ivan T. Dimov. "A Parallel Monte Carlo Method for Electron Quantum Kinetic Equation." In Large-Scale Scientific Computing, 153–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24588-9_16.

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Lavorel, J. "A Monte Carlo method for the simulation of kinetic models." In Current topics in photosynthesis, 271–81. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4412-1_25.

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Kehr, K. W., and K. Binder. "Simulation of Diffusion in Lattice Gases and Related Kinetic Phenomena." In Applications of the Monte Carlo Method in Statistical Physics, 181–221. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-51703-7_6.

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10

Martin, Georges, and Frédéric Soisson. "Kinetic Monte Carlo Method to Model Diffusion Controlled Phase Transformations in the Solid State." In Handbook of Materials Modeling, 2223–48. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-3286-2_115.

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Тези доповідей конференцій з теми "Kinetic Monte Carlo Methods"

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Yang, Xue, and Wasiu O. Oyeniyi. "Kinetic Monte Carlo Simulation of Hydrogen Diffusion in Tungsten." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60352.

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This research developed a Kinetic Monte Carlo (KMC) method for simulating hydrogen diffusion in tungsten bulk. The KMC inputs such as diffusion paths and energy barriers are taken from DFT calculation results from the literatures. In this simulation model, stable hydrogen interstitial sites in tungsten are the tetrahedral sites on each surface of the bcc lattice, and each site has four tetrahedral neighboring sites, with two neighbors on the same lattice surface and the other two on the adjacent two perpendicular surfaces. A MATLAB script has been developed to perform the diffusion modeling for any given hydrogen concentration and substrate temperature. To compare the simulation results with experiment measurements, modeling configuration of low hydrogen concentration and temperature of 300 K to 2500 K mirroring the experiment conditions was used. The calculated diffusion coefficients at various temperatures match the experiment reference very well. The calculated diffusion coefficients are also fitted to the Arrhenius equation as: D [m2/s] = 5.59×10−7 exp(−0.426/kBT)
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2

Hua, L., O. Hovorka, R. M. Ferguson, R. W. Chantrell, and K. M. Krishnan. "MPI tracer magnetization simulated using a Kinetic Monte Carlo method." In 2013 International Workshop on Magnetic Particle Imaging (IWMPI). IEEE, 2013. http://dx.doi.org/10.1109/iwmpi.2013.6528374.

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3

Wang, Yan. "Reliable Kinetic Monte Carlo Simulation Based on Random Set Sampling." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48575.

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To simulate the dynamic behaviors of large molecular systems, approaches that solve ordinary differential equations such as molecular dynamics (MD) simulation may become inefficient. The kinetic Monte Carlo (KMC) method as the alternative has been widely used in simulating rare events such as chemical reactions or phase transitions. Yet lack of complete knowledge of transitions and the associated rates is one major challenge for accurate KMC predictions. In this paper, a reliable KMC (R-KMC) mechanism is proposed to improve the robustness of KMC results, where propensities are interval estimates instead of precise numbers and sampling is based on random sets instead of random numbers. A multi-event algorithm is developed and implemented. The weak convergence of the multi-event algorithm towards traditional KMC is demonstrated with a proposed generalized Chapman-Kolmogorov Equation.
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"Kinetic Equation Method and Monte Carlo Method for Charge Carriers Dynamics Description in Diamond." In International Conference on Photonics, Optics and Laser Technology. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004809801220126.

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5

Jo, YuGwon, Bumhee Cho, and Nam Zin Cho. "Nuclear reactor transient analysis via a quasi-static kinetics Monte Carlo method." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015 (ICCMSE 2015). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4938774.

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Alhat, Devendra, Vernet Lasrado, and Yan Wang. "A Review of Recent Phase Transition Simulation Methods: Saddle Point Search." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49411.

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A review of saddle point search methods on a potential energy surface is presented in this paper. Finding saddle points on a complex potential energy surface is the major challenge in modeling and simulating the kinetics of first-order phase transitions. Once the saddle points have been identified and the activation energy for the transition is known, one can apply the kinetic Monte Carlo method to simulate the transition process. We consider some factors while reviewing the methods, such as whether the solution is global, the knowledge of the Hessian during the search, the capability to locate multiple saddle points and higher order saddle points, the kind of approximations used for potential energy surface, if any; and the convergence of the methods.
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Landon, Colin, and Nicolas G. Hadjiconstantinou. "Low-Variance Monte Carlo Simulation of Thermal Transport in Graphene." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87957.

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Due to its unique thermal properties, graphene has generated considerable interest in the context of thermal management applications. In order to correctly treat heat transfer in this material, while still reaching device-level length and time scales, a kinetic description, such as the Boltzmann transport equation, is typically required. We present a Monte Carlo method for obtaining numerical solutions of this description that dramatically outperforms traditional Monte Carlo approaches by simulating only the deviation from equilibrium. We validate the simulation method using an analytical solution of the Boltzmann equation for long graphene nanoribbons; we also use this result to characterize the error associated with previous approximate solutions of this problem.
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Shang, Xiaotong, Guanlin Shi, and Kan Wang. "One Step Method for Multigroup Adjoint Neutron Flux Through Continuous Energy Monte Carlo Calculation." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-82185.

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The adjoint neutron flux is vital in the analysis of reactor kinetics parameters and reactor transient events. Both deterministic and Monte Carlo methods have been developed for the adjoint neutron flux calculation on the basis of multi-group cross sections which may vary significantly among different types of reactors. The iterated fission probability (IFP) method is introduced to calculate the neutron importance which is able to represent the adjoint neutron flux in continuous energy problem and have been applied to the calculation of kinetics parameters. However, the adjoint neutron flux can’t be obtained directly and applied to both Monte Carlo transient event analysis and deterministic methods. In this study, a method based on IFP is studied and implemented in Monte Carlo code RMC. The multi-group adjont neutron flux can be obtained directly through the discretization of energy and space with the modification of fission neutrons through continuous energy Monte Carlo calculations. The obtained multi-group adjoint neutron flux can be used in both Monte Carlo transient analysis and deterministic methods.
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Qiu, Yishu, Manuele Aufiero, Kan Wang, and Massimiliano Fratoni. "Generalized Sensitivity Analysis With Continuous-Energy Monte Carlo Code RMC." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60473.

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A new capability for computing sensitivity coefficients of bilinear response functions has been developed in the Reactor Monte Carlo code RMC based on the collision history-based method. Originally implemented in the Monte Carlo code SERPENT2 in the frame of Delta-tracking technique, this method computes the perturbation of particle weight based on the concept of accepted events and rejected events. The implementation of this method in RMC is based on ray-tracking technique. The new capability in RMC has been verified by comparing sensitivity coefficients of adjoint-weighted kinetic parameters including effective prompt lifetime and effective delayed neutron fraction from SERPENT2 as well as two deterministic codes based on Equivalent Generalized Perturbation Theory (EGPT), TSUNAMI-1D and SUSD3D, through two fast metallic systems, the Jezebel and flattop problems. Good agreement among RMC, SERPENT2, SUSD3D and TSUNAMI-1D (EGPT) is observed.
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Cai, Linlin, Yudi Zhao, Wangyong Chen, Peng Huang, Xiaoyan Liu, and Xing Zhang. "Self-heating aware EM Reliability Prediction of Advanced CMOS Technology by Kinetic Monte Carlo Method." In 2019 IEEE 26th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). IEEE, 2019. http://dx.doi.org/10.1109/ipfa47161.2019.8984791.

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Звіти організацій з теми "Kinetic Monte Carlo Methods"

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Hehr, Brian Douglas. LDRD Report : Analysis of Defect Clustering in Semiconductors using Kinetic Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1465520.

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Vogel, Thomas. Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148317.

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Hungerford, Aimee L. (U) Introduction to Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1351179.

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Bulatov, V., T. Oppelstrup, and M. Athenes. A new class of accelerated kinetic Monte Carlo algorithms. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1033740.

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Brown, Forrest B. Advanced Computational Methods for Monte Carlo Calculations. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417155.

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Caflisch, Russel E. Rarefied Gas Dynamics and Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada295375.

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Creutz, M. Lattice gauge theory and Monte Carlo methods. Office of Scientific and Technical Information (OSTI), November 1988. http://dx.doi.org/10.2172/6530895.

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Wirth, B. D., M. J. Caturla, and Diaz de la Rubia, T. Modeling and Computer Simulation: Molecular Dynamics and Kinetic Monte Carlo. Office of Scientific and Technical Information (OSTI), October 2000. http://dx.doi.org/10.2172/792741.

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Jerome Spanier. Third International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC98). Office of Scientific and Technical Information (OSTI), March 1999. http://dx.doi.org/10.2172/761782.

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Owen, Richard Kent. Quantum Monte Carlo methods and lithium cluster properties. Office of Scientific and Technical Information (OSTI), December 1990. http://dx.doi.org/10.2172/10180548.

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