Relevant bibliographies by topics / Stochastic simulation algorithms

Academic literature on the topic 'Stochastic simulation algorithms'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Stochastic simulation algorithms"

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Stutz, Timothy C., Alfonso Landeros, Jason Xu, Janet S. Sinsheimer, Mary Sehl, and Kenneth Lange. "Stochastic simulation algorithms for Interacting Particle Systems." PLOS ONE 16, no. 3 (March 2, 2021): e0247046. http://dx.doi.org/10.1371/journal.pone.0247046.

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Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source programming language Julia. We also apply our algorithms to several complex spatial stochastic phenomena. including a rock-paper-scissors game, cancer growth in response to immunotherapy, and lipid oxidation dynamics. Our approach aids in standardizing mathematical models and in generating hypotheses based on concrete mechanistic behavior across a wide range of observed spatial phenomena.
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Mooasvi, Azam, and Adrian Sandu. "APPROXIMATE EXPONENTIAL ALGORITHMS TO SOLVE THE CHEMICAL MASTER EQUATION." Mathematical Modelling and Analysis 20, no. 3 (June 2, 2015): 382–95. http://dx.doi.org/10.3846/13926292.2015.1048760.

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This paper discusses new simulation algorithms for stochastic chemical kinetics that exploit the linearity of the chemical master equation and its matrix exponential exact solution. These algorithms make use of various approximations of the matrix exponential to evolve probability densities in time. A sampling of the approximate solutions of the chemical master equation is used to derive accelerated stochastic simulation algorithms. Numerical experiments compare the new methods with the established stochastic simulation algorithm and the tau-leaping method.
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Altıntan, Derya, Vi̇lda Purutçuoğlu, and Ömür Uğur. "Impulsive Expressions in Stochastic Simulation Algorithms." International Journal of Computational Methods 15, no. 01 (September 27, 2017): 1750075. http://dx.doi.org/10.1142/s021987621750075x.

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Jumps can be seen in many natural processes. Classical deterministic modeling approach explains the dynamical behavior of such systems by using impulsive differential equations. This modeling strategy assumes that the dynamical behavior of the whole system is deterministic, continuous, and it adds jumps to the state vector at certain times. Although deterministic approach is satisfactory in many cases, it is a well-known fact that stochasticity or uncertainty has crucial importance for dynamical behavior of many others. In this study, we propose to include this abrupt change in the stochastic modeling approach, beside the deterministic one. In our model, we introduce jumps to chemical master equation and use the Gillespie direct method to simulate the evolutionary system. To illustrate the idea and distinguish the differences, we present some numerically solved examples.
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Konopel'kin, M. Yu, S. V. Petrov, and D. A. Smirnyagina. "Implementation of stochastic signal processing algorithms in radar CAD." Russian Technological Journal 10, no. 5 (October 21, 2022): 49–59. http://dx.doi.org/10.32362/2500-316x-2022-10-5-49-59.

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Objectives. In 2020, development work on the creation of a Russian computer-assisted design system for radars (radar CAD) was completed. Radar CAD provides extensive opportunities for creating simulation models for developing the hardware-software complex of radar algorithms, which take into account the specific conditions of aerospace environment observation. The purpose of the present work is to review and demonstrate the capabilities of radar CAD in terms of implementing and testing algorithms for processing stochastic signals.Methods. The work is based on the mathematical apparatus of linear algebra. Analysis of algorithms characteristics was carried out using the simulation method.Results. A simulation model of a sector surveillance radar with a digital antenna array was created in the radar CAD visual functional editor. The passive channel included the following algorithms: algorithm for detecting stochastic signals; algorithm for estimating the number of stochastic signals; direction finding algorithm for stochastic signal sources; adaptive spatial filtering algorithm. In the process of simulation, the algorithms for detecting and estimating the number of stochastic signals produced a correct detection sign and an estimate of the number of signals. The direction-finding algorithm estimated the angular position of the sources with an accuracy of fractions of degrees. The adaptive spatial filtering algorithm suppressed interfering signals to a level below the antenna's intrinsic noise power.Conclusions. The processing of various types of signals can be simulated in detail on the basis of the Russian radar CAD system for the development of functional radar models. According to the results of the simulation, coordinates of observing objects were obtained and an assessment of the effectiveness of the algorithms was given. The obtained results are fully consistent with the theoretical prediction. The capabilities of radar CAD systems demonstrated in this work can be used by specialists in the field of radar and signal processing.
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Wieder, Nicolas, Rainer H. A. Fink, and Frederic von Wegner. "Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics." Journal of Biomedicine and Biotechnology 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/572492.

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In simulations of chemical systems, the main task is to find an exact or approximate solution of thechemical master equation(CME) that satisfies certain constraints with respect to computation time and accuracy. WhileBrownian motionsimulations of single molecules are often too time consuming to represent the mesoscopic level, the classicalGillespie algorithmis a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains.Gillespie's algorithmcan be approximated via thetau-leapmethod and thechemical Langevin equation(CLE). Both methods lead to a substantial acceleration in computation time and a relatively small decrease in accuracy. Elimination of the noise terms leads to the classical, deterministic reaction rate equations (RRE). For complex multiscale systems, hybrid simulations are increasingly proposed to combine the advantages of stochastic and deterministic algorithms. An often used exemplary cell type in this context are striated muscle cells (e.g., cardiac and skeletal muscle cells). The properties of these cells are well described and they express many common calcium-dependent signaling pathways. The purpose of the present paper is to provide an overview of the aforementioned simulation approaches and their mutual relationships in the spectrum ranging from stochastic to deterministic algorithms.
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Ding, Liangliang, Jingyuan Zhou, Wenhui Tang, Xianwen Ran, and Ye Cheng. "Research on the Crushing Process of PELE Casing Material Based on the Crack-Softening Algorithm and Stochastic Failure Algorithm." Materials 11, no. 9 (August 30, 2018): 1561. http://dx.doi.org/10.3390/ma11091561.

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In order to more realistically reflect the penetrating and crushing process of a PELE (Penetration with Enhanced Lateral Efficiency) projectile, the stochastic failure algorithm and crack-softening algorithm were added to the corresponding material in this paper. According to the theoretical analysis of the two algorithms, the material failure parameters (stochastic constant γ, fracture energy Gf, and tensile strength σT) were determined. Then, four sets of simulation conditions ((a) no crack softening, (b) no stochastic failure, (c) no crack softening and no stochastic failure, and (d) crack softening and stochastic failure) were designed to qualitatively describe the influences of the failure algorithms, which were simulated by the finite element analysis software AUTODYN. The qualitative comparison results indicate that the simulation results after adding the two algorithms were closer to the actual situation. Finally, ten groups of simulation conditions were designed to quantitatively analyze the coincidence degree between the simulation results and the experimental results by means of two parameters: the residual velocity of the projectile and the maximum radial velocity of fragments. The results show that the simulation results coincide well with the experimental results and the errors were small. Therefore, the ideas proposed in this paper are scientific, and the conclusions obtained can provide guidance for engineering research.
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Bhatnagar, Shalabh, Vivek Kumar Mishra, and Nandyala Hemachandra. "Stochastic Algorithms for Discrete Parameter Simulation Optimization." IEEE Transactions on Automation Science and Engineering 8, no. 4 (October 2011): 780–93. http://dx.doi.org/10.1109/tase.2011.2159375.

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XU, ZI, YINGYING LI, and XINGFANG ZHAO. "SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM." Asia-Pacific Journal of Operational Research 31, no. 04 (August 2014): 1450026. http://dx.doi.org/10.1142/s0217595914500262.

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This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimization problem, several unconstrained optimization problems and one typical simulation-based optimization problem, i.e., (s, S)-inventory problem.
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Braun, Daniel, and Ronny Müller. "Stochastic emulation of quantum algorithms." New Journal of Physics 24, no. 2 (February 1, 2022): 023028. http://dx.doi.org/10.1088/1367-2630/ac4b0f.

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Abstract Quantum algorithms profit from the interference of quantum states in an exponentially large Hilbert space and the fact that unitary transformations on that Hilbert space can be broken down to universal gates that act only on one or two qubits at the same time. The former aspect renders the direct classical simulation of quantum algorithms difficult. Here we introduce higher-order partial derivatives of a probability distribution of particle positions as a new object that shares these basic properties of quantum mechanical states needed for a quantum algorithm. Discretization of the positions allows one to represent the quantum mechanical state of n bit qubits by 2(n bit + 1) classical stochastic bits. Based on this, we demonstrate many-particle interference and representation of pure entangled quantum states via derivatives of probability distributions and find the universal set of stochastic maps that correspond to the quantum gates in a universal gate set. We prove that the propagation via the stochastic map built from those universal stochastic maps reproduces up to a prefactor exactly the evolution of the quantum mechanical state with the corresponding quantum algorithm, leading to an automated translation of a quantum algorithm to a stochastic classical algorithm. We implement several well-known quantum algorithms, analyse the scaling of the needed number of realizations with the number of qubits, and highlight the role of destructive interference for the cost of the emulation.
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Wang, Dongqing, Tong Shan, and Rui Ding. "DATA FILTERING BASED STOCHASTIC GRADIENT ALGORITHMS FOR MULTIVARIABLE CARAR-LIKE SYSTEMS." Mathematical Modelling and Analysis 18, no. 3 (June 1, 2013): 374–85. http://dx.doi.org/10.3846/13926292.2013.804889.

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This paper considers identification problems for a multivariable controlled autoregressive system with autoregressive noises. A hierarchical generalized stochastic gradient algorithm and a filtering based hierarchical stochastic gradient algorithm are presented to estimate the parameter vectors and parameter matrix of such multivariable colored noise systems, by using the hierarchical identification principle. The simulation results show that the proposed hierarchical gradient estimation algorithms are effective.
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Dissertations / Theses on the topic "Stochastic simulation algorithms"

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Hu, Liujia. "Convergent algorithms in simulation optimization." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54883.

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It is frequently the case that deterministic optimization models could be made more practical by explicitly incorporating uncertainty. The resulting stochastic optimization problems are in general more difficult to solve than their deterministic counterparts, because the objective function cannot be evaluated exactly and/or because there is no explicit relation between the objective function and the corresponding decision variables. This thesis develops random search algorithms for solving optimization problems with continuous decision variables when the objective function values can be estimated with some noise via simulation. Our algorithms will maintain a set of sampled solutions, and use simulation results at these solutions to guide the search for better solutions. In the first part of the thesis, we propose an Adaptive Search with Resampling and Discarding (ASRD) approach for solving continuous stochastic optimization problems. Our ASRD approach is a framework for designing provably convergent algorithms that are adaptive both in seeking new solutions and in keeping or discarding already sampled solutions. The framework is an improvement over the Adaptive Search with Resampling (ASR) method of Andradottir and Prudius in that it spends less effort on inferior solutions (the ASR method does not discard already sampled solutions). We present conditions under which the ASRD method is convergent almost surely and carry out numerical studies aimed at comparing the algorithms. Moreover, we show that whether it is beneficial to resample or not depends on the problem, and analyze when resampling is desirable. Our numerical results show that the ASRD approach makes substantial improvements on ASR, especially for difficult problems with large numbers of local optima. In traditional simulation optimization problems, noise is only involved in the objective functions. However, many real world problems involve stochastic constraints. Such problems are more difficult to solve because of the added uncertainty about feasibility. The second part of the thesis presents an Adaptive Search with Discarding and Penalization (ASDP) method for solving continuous simulation optimization problems involving stochastic constraints. Rather than addressing feasibility separately, ASDP utilizes the penalty function method from deterministic optimization to convert the original problem into a series of simulation optimization problems without stochastic constraints. We present conditions under which the ASDP algorithm converges almost surely from inside the feasible region, and under which it converges to the optimal solution but without feasibility guarantee. We also conduct numerical studies aimed at assessing the efficiency and tradeoff under the two different convergence modes. Finally, in the third part of the thesis, we propose a random search method named Gaussian Search with Resampling and Discarding (GSRD) for solving simulation optimization problems with continuous decision spaces. The method combines the ASRD framework with a sampling distribution based on a Gaussian process that not only utilizes the current best estimate of the optimal solution but also learns from past sampled solutions and their objective function observations. We prove that our GSRD algorithm converges almost surely, and carry out numerical studies aimed at studying the effects of utilizing the Gaussian sampling strategy. Our numerical results show that the GSRD framework performs well when the underlying objective function is multi-modal. However, it takes much longer to sample solutions, especially in higher dimensions.
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Qureshi, Sumaira Ejaz. "Comparative study of simulation algorithms in mapping spaces of uncertainty /." St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16450.pdf.

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MOSCA, ETTORE. "Membrane systems and stochastic simulation algorithms for the modelling of biological systems." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19296.

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Membrane Computing is a branch of computer science that was born after the introduction of Membrane Systems (or P systems) by a seminal paper by Gh. Paun. Membrane systems are computing devices inspired by the structure and functioning of living cells as well as from the way the cells are organized in tissues and higher order structures. The aim of membrane computing is to abstract computing ideas and models imitating these products of natural evolution. A typical membrane system is composed by a number of regions surrounded by membranes; regions contains multisets of objects (molecules) and rules (cellular processes) that specify how objects must be re-written and moved among regions. In spite of the fact that the initial primary goal of membrane systems concerned computability theory, the properties of membrane systems such as compartmentalisation, modularity, scalability/extensibility, understandability, programmability and discreteness promoted their use for an important task of the current scientific research: the modelling of biological systems (the topic “systems biology, including modelling of complex systems” has now appeared explicitly in the Seventh Framework Programme of the European Community for research, technological development and demonstration activities). To accomplish this task some features of membrane systems (such as nondeterminism and maximal parallelism) have to be mitigated while other properties have to be considered (e.g. description of the time evolution of the modelled system) to ensure the accurateness of the results gained with the models. Many approaches for the modelling and simulation of biological systems exist and can be classified according to features such as continuous/discrete, deterministic/stochastic, macroscopic/mesoscopic/microscopic, predictive/explorative, quantitative/qualitative and so on. Recently, stochastic methods have gained more attention since many biological processes, such as gene transcription and translation into proteins, are controlled by noisy mechanisms. Considering the branch of modelling focused at the molecular level and dealing with systems of biochemical processes (e.g. a signalling or metabolic pathway inside a living cell), an important class of stochastic simulation methods is the one inspired by the Gillespie's stochastic simulation algorithm (SSA). This method provides exact numerical realisations of the stochastic process defined by the chemical master equation. A series of methods (e.g. next reaction method, tau leaping, next subvolume method) and software (StochKit and MesoRD), belonging to this class, were developed for the modelling and simulation of homogeneous and/or reaction-diffusion (mesoscopic) systems. A stochastic approach that couples the expressive power of a membrane system (and more precisely of Dynamical Probabilistic P systems or DPPs) with a modified version of the tau leaping method in order to quantitatively describe the evolution of multi-compartmental systems in time is the tau-DPP approach. Both current membrane systems variants and stochastic methods inspired by the SSA lack the consideration of some properties of living cells, such as the molecular crowding or the presence of membrane potential differences. Thus, the current versions of these formalisms and computational methods do not allow to model and simulate all those biological processes where these features play an essential role. A common task in the field of stochastic simulations (mainly based on numerical rather than analytical solutions) is the repetition of a large number of simulations. This activity is required, for example, to characterise the dynamics of the modelled system and by some parameter estimation or sensitivity analysis algorithms. In this thesis we extend the tau-DPP approach taking into account additional properties of living cells in order to expand tau-DPPs modelling (and simulation) capabilities to a broader set of scenarios. Within this scope, we also exploit the main European grid computing platform as a computational platform usable to compute stochastic simulations, developing a framework specific to this purpose, able to manage a large number of simulations of stochastic models. In our formalism, we considered the explicit modelling of both the objects' (or molecules) and membranes' (or compartments) volume occupation, as mandated by the mutual impenetrability of molecules. As a consequence, the dynamics of the system are affected by the availability of free space. In living cells, for example, molecular crowding has important effects such as anomalous diffusion, variation of reaction rates and spatial segregation, which have significant consequences on the dynamics of cellular processes. At a theoretical level, we demonstrated that the explicit consideration of the volume occupation of objects and membranes (and their consequences on the system's evolution) does not reduce the computational universality of membrane systems. We achieved this aim showing that is it possible to simulate a deterministic Turing machine and that the volume required by the membrane systems that carry out this task is a linear function of the space required by the Turing machine. After this, we presented a novel version of both membrane systems (designated as Stau-DPPs) and stochastic simulation algorithm (Stau-DPP algorithm) considering the property of mutual impenetrability of molecules. In addition, we made the communication of objects independent from the system's structure in order to obtain a strong expressive power. After showing that the Stau-DPP algorithm can accurately reproduce particle diffusion (in a comparison with the heat equation), we presented two test cases to illustrate that Stau-DPPs can effectively capture some effects of crowding, namely the reduction of particle diffusion rate and the increase of reaction rate, considering a bidimensional discrete space domain. We presented also a test case to illustrate that the strong expressive power of Stau-DPPs allows the modelling and simulation (by means of the Stau-DPP algorithm) of processes taking place in structured environments; more precisely, we modelled and simulate the diffusion of molecules enhanced by the presence of a structure resembling the role of a microtubule (a sort of “railway” for intracellular trafficking) in living cells. Subsequently, we further extended Stau-DPPs and the respective evolution algorithm to explicitly consider the membrane potential difference and its effect over charged particles and voltage gated channel (VGC, a particular type of membrane protein) state transitions. In fact, the membrane potential difference exhibited by biological membranes plays a crucial role in many cellular processes (e.g. action potential and synaptic signalling cascades). Similarly to what we did for the Stau-DPPs, we presented the novel version of both the membrane systems (designated as EStau-DPPs) and the stochastic simulation algorithm (EStau-DPP algorithm) to capture the additional properties we had considered. In order to describe the probability of charged particle diffusion in a discrete space domain, we defined a propensity function starting from the deterministic and continuous description of charged particle diffusion due to an electric potential gradient. We showed by means of a focused test case that a model for ion diffusion between two regions, in which the number of ions is maintained at two different constant values and where an electric potential difference is available, correctly reaches the expected state as predicted by the Nernst equation. To describe the probability of transition between two VGC states, we derived a propensity function taking into consideration the Boltzmann-Maxwell distribution. We considered a model describing the state transitions of a VGC and we showed that the model predictions are in close agreement with the experimental data collected from literature. Lastly, we presented the framework to manage a large number of stochastic simulations on a grid computing platform. While creating this framework, we considered the parameter sweep application (PSA) approach, in which an application is run a large number of times with different parameter values. We ran a set of PSAs concerning the simulations of a stochastic bacterial chemotaxis model and the computation of the difference between the dynamics of one of its components (as a consequence of model parameter variation) compared to a reference dynamics of the same component. We then used this set of PSAs to evaluate the performance of the EGEE project's grid infrastructure (Enabling Grid for the E-sciencE). On the one hand, the EGEE grid proved to be a useful solution for the distribution of PSAs concerning the stochastic simulations of biochemical systems. The platform demonstrated its efficiency in the context of our middle-size test, and considering that the more intensive the computation, the more scalable the infrastructure, grid computing can be a suitable technology for large scale biological models analysis. On the other hand, the use of a distributed file system, the granularity of the jobs and the heterogeneity of the resources can present issues. In conclusion, in this thesis we extended previous membrane systems variants and stochastic simulation methods for the analysis of biological systems, and exploited grid computing for large scale stochastic simulations. Stau-DPPs and EStau-DPPs (and their respective algorithms to calculate the temporal evolution) increase the set of biological systems that can be investigated \textit{in silico in the context of the stochastic methods inspired by the SSA. In fact, compared to its precursor approach (tau-DPPs), Stau-DPPs allow the stochastic and discrete analysis of crowded systems, structured geometries, while EStau-DPPs also take into account some electric properties (membrane electric potential and its consequences), enabling, for example, the modelling of cellular signalling systems influenced by the membrane potential. In future, we plan to improve both the formalisations and the algorithms that we presented in this thesis. For example, Stau-DPPs can not model and simulate objects bigger than a single compartment, which conversely can be convenient for the analysis of big crowding agents in a tightly discretised space domain; instead, EStau-DPPs are, for instance, currently limited to the modelling of systems composed by two compartments separated by a boundary that can be assumed to act as a capacitor (e.g biological membranes). Moreover, we plan to optimize the parallel (MPI) implementation of both the Stau-DPP and EStau-DPP algorithms, which are presently based on a one-to-one relationship between processes and compartments, a limiting factor for the simulation of discrete spaces composed by a high number of compartments. Lastly, as grid computing demonstrated to be a useful approach to handle a large number of simulations, we plan to develop a solution to handle the simulations required in the context of sensitivity analysis.
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Xu, Guanglei. "Adiabatic processes, noise, and stochastic algorithms for quantum computing and quantum simulation." Thesis, University of Strathclyde, 2018. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=30919.

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Rapid developments in experiments provide promising platforms for realising quantum computation and quantum simulation. This, in turn, opens new possibilities for developing useful quantum algorithms and explaining complex many-body physics. The advantages of quantum computation have been demonstrated in a small range of subjects, but the potential applications of quantum algorithms for solving complex classical problems are still under investigation. Deeper understanding of complex many-body systems can lead to realising quantum simulation to study systems which are inaccessible by other means. This thesis studies different topics of quantum computation and quantum simulation. The first one is improving a quantum algorithm in adiabatic quantum computing, which can be used to solve classical problems like combinatorial optimisation problems and simulated annealing. We are able to reach a new bound of time cost for the algorithm which has a potential to achieve a speed up over standard adiabatic quantum computing. The second topic is to understand the amplitude noise in optical lattices in the context of adiabatic state preparation and the thermalisation of the energy introduced to the system. We identify regimes where introducing certain type of noise in experiments would improve the final fidelity of adiabatic state preparation, and demonstrate the robustness of the state preparation to imperfect noise implementations. We also discuss the competition between heating and dephasing effects, the energy introduced by non-adiabaticity and heating, and the thermalisation of the system after an application of amplitude noise on the lattice. The third topic is to design quantum algorithms to solve classical problems of fluid dynamics. We develop a quantum algorithm based around phase estimation that can be tailored to specific fluid dynamics problems and demonstrate a quantum speed up over classical Monte Carlo methods. This generates new bridge between quantum physics and fluid dynamics engineering, can be used to estimate the potential impact of quantum computers and provides feedback on requirements for implementing quantum algorithms on quantum devices.
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Park, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.

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In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to zero almost surely for any strictly feasible solution under certain conditions. For a feasible solution located on the boundary of feasible and infeasible regions, the sequence converges to zero either with high probability or almost surely. As a result, a DOvS algorithm combined with PFM performs well even when optimal solutions are tight or nearly tight. Second, we design an optimal water quality monitoring network for river systems. The problem is to find the optimal location of a finite number of monitoring devices, minimizing the expected detection time of a contaminant spill event while guaranteeing good detection reliability. When uncertainties in spill and rain events are considered, both the expected detection time and detection reliability need to be estimated by stochastic simulation. This problem is formulated as a stochastic DOvS problem with the objective of minimizing expected detection time and with a stochastic constraint on the detection reliability; and it is solved by a DOvS algorithm combined with PFM. Finally, we improve PFM by combining it with an approximate budget allocation procedure. We revise an existing optimal budget allocation procedure so that it can handle active constraints and satisfy necessary conditions for the convergence of PFM.
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Yarmolskyy, Oleksandr. "Využití distribuovaných a stochastických algoritmů v síti." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-370918.

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This thesis deals with the distributed and stochastic algorithms including testing their convergence in networks. The theoretical part briefly describes above mentioned algorithms, including their division, problems, advantages and disadvantages. Furthermore, two distributed algorithms and two stochastic algorithms are chosen. The practical part is done by comparing the speed of convergence on various network topologies in Matlab.
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Zhang, Chao Ph D. Massachusetts Institute of Technology. "Computationally efficient offline demand calibration algorithms for large-scale stochastic traffic simulation models." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120639.

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Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 168-181).
This thesis introduces computationally efficient, robust, and scalable calibration algorithms for large-scale stochastic transportation simulators. Unlike a traditional "black-box" calibration algorithm, a macroscopic analytical network model is embedded through a metamodel simulation-based optimization (SO) framework. The computational efficiency is achieved through the analytical network model, which provides the algorithm with low-fidelity, analytical, differentiable, problem-specific structural information and can be efficiently evaluated. The thesis starts with the calibration of low-dimensional behavioral and supply parameters, it then addresses a challenging high-dimensional origin-destination (OD) demand matrix calibration problem, and finally enhances the OD demand calibration by taking advantage of additional high-resolution traffic data. The proposed general calibration framework is suitable to address a broad class of calibration problems and has the flexibility to be extended to incorporate emerging data sources. The proposed algorithms are first validated on synthetic networks and then tested through a case study of a large-scale real-world network with 24,335 links and 11,345 nodes in the metropolitan area of Berlin, Germany. Case studies indicate that the proposed calibration algorithms are computationally efficient, improve the quality of solutions, and are robust to both the initial conditions and to the stochasticity of the simulator, under a tight computational budget. Compared to a traditional "black-box" method, the proposed method improves the computational efficiency by an average of 30%, as measured by the total computational runtime, and simultaneously yields an average of 70% improvement in the quality of solutions, as measured by its objective function estimates, for the OD demand calibration. Moreover, the addition of intersection turning flows further enhances performance by improving the fit to field data by an average of 20% (resp. 14%), as measured by the root mean square normalized (RMSN) errors of traffic counts (resp. intersection turning flows).
by Chao Zhang.
Ph. D. in Transportation
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Chen, Si. "Design of Energy Storage Controls Using Genetic Algorithms for Stochastic Problems." UKnowledge, 2015. http://uknowledge.uky.edu/ece_etds/80.

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A successful power system in military applications (warship, aircraft, armored vehicle etc.) must operate acceptably under a wide range of conditions involving different loading configurations; it must maintain war fighting ability and recover quickly and stably after being damaged. The introduction of energy storage for the power system of an electric warship integrated engineering plant (IEP) may increase the availability and survivability of the electrical power under these conditions. Herein, the problem of energy storage control is addressed in terms of maximizing the average performance. A notional medium-voltage dc system is used as the system model in the study. A linear programming model is used to simulate the power system, and two sets of states, mission states and damage states, are formulated to simulate the stochastic scenarios with which the IEP may be confronted. A genetic algorithm is applied to the design of IEP to find optimized energy storage control parameters. By using this algorithm, the maximum average performance of power system is found.
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Shang, Xiaocheng. "Extended stochastic dynamics : theory, algorithms, and applications in multiscale modelling and data science." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20422.

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This thesis addresses the sampling problem in a high-dimensional space, i.e., the computation of averages with respect to a defined probability density that is a function of many variables. Such sampling problems arise in many application areas, including molecular dynamics, multiscale models, and Bayesian sampling techniques used in emerging machine learning applications. Of particular interest are thermostat techniques, in the setting of a stochastic-dynamical system, that preserve the canonical Gibbs ensemble defined by an exponentiated energy function. In this thesis we explore theory, algorithms, and numerous applications in this setting. We begin by comparing numerical methods for particle-based models. The class of methods considered includes dissipative particle dynamics (DPD) as well as a newly proposed stochastic pairwise Nosé-Hoover-Langevin (PNHL) method. Splitting methods are developed and studied in terms of their thermodynamic accuracy, two-point correlation functions, and convergence. When computational efficiency is measured by the ratio of thermodynamic accuracy to CPU time, we report significant advantages in simulation for the PNHL method compared to popular alternative schemes in the low-friction regime, without degradation of convergence rate. We propose a pairwise adaptive Langevin (PAdL) thermostat that fully captures the dynamics of DPD and thus can be directly applied in the setting of momentum-conserving simulation. These methods are potentially valuable for nonequilibrium simulation of physical systems. We again report substantial improvements in both equilibrium and nonequilibrium simulations compared to popular schemes in the literature. We also discuss the proper treatment of the Lees-Edwards boundary conditions, an essential part of modelling shear flow. We also study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. These methods are important in multiscale modelling and in the design of new machine learning algorithms for inference and parameterization for large datasets, challenges which are increasingly important in "big data" applications. In addition to providing a more comprehensive discussion of the foundations of these methods, we propose a new numerical method for the adaptive Langevin/stochastic gradient Nosé-Hoover thermostat that achieves a dramatic improvement in numerical efficiency over the most popular stochastic gradient methods reported in the literature. We demonstrate that the newly established method inherits a superconvergence property (fourth order convergence to the invariant measure for configurational quantities) recently demonstrated in the setting of Langevin dynamics. Furthermore, we propose a covariance-controlled adaptive Langevin (CCAdL) thermostat that can effectively dissipate parameter-dependent noise while maintaining a desired target distribution. The proposed method achieves a substantial speedup over popular alternative schemes for large-scale machine learning applications.
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Egilmez, Gokhan. "Stochastic Cellular Manufacturing System Design and Control." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1354351909.

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Books on the topic "Stochastic simulation algorithms"

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Asmussen, Søren, and Peter W. Glynn. Stochastic Simulation: Algorithms and Analysis. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69033-9.

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Asmussen, Søren. Stochastic simulation: Algorithms and analysis. New York: Springer, 2011.

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Öttinger, Hans Christian. Stochastic processes in polymeric fluids: Tools and examples for developing simulation algorithms. Berlin: Springer, 1996.

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Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. London: Springer London, 2013.

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Judd, Kenneth L. One-node quadrature beats monte carlo: A generalized stochastic simulation algorithm. Cambridge, MA: National Bureau of Economic Research, 2011.

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Shi, Yixi. Rare Events in Stochastic Systems: Modeling, Simulation Design and Algorithm Analysis. [New York, N.Y.?]: [publisher not identified], 2013.

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Dieter, Fiems, Vincent Jean-Marc, and SpringerLink (Online service), eds. Analytical and Stochastic Modeling Techniques and Applications: 19th International Conference, ASMTA 2012, Grenoble, France, June 4-6, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Glynn, Peter W., and Søren Asmussen. Stochastic Simulation: Algorithms and Analysis. Springer London, Limited, 2007.

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Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability). Springer, 2007.

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Öttinger, Hans C. Stochastic Processes in Polymeric Fluids: Tools and Examples for Developing Simulation Algorithms. Springer, 1995.

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Book chapters on the topic "Stochastic simulation algorithms"

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Kashtanov, Y. N., and I. N. Kuchkova. "Monte Carlo Algorithms For Neumann Boundary Value Problem Using Fredholm Representation." In Advances in Stochastic Simulation Methods, 17–28. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5_2.

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Behnke, Henning, Michael Kolonko, Ulrich Mertins, and Stefan Schnitter. "Optimization and Simulation: Sequential Packing of Flexible Objects Using Evolutionary Algorithms." In Stochastic Algorithms: Foundations and Applications, 145–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45322-9_10.

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van den Akker, Marjan, Kevin van Blokland, and Han Hoogeveen. "Finding Robust Solutions for the Stochastic Job Shop Scheduling Problem by Including Simulation in Local Search." In Experimental Algorithms, 402–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38527-8_35.

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Conclusion and Further Research Directions." In Sine Cosine Algorithm for Optimization, 105–6. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_6.

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AbstractThe increasing complexity of real-world optimization problems demands fast, robust, and efficient meta-heuristic algorithms. The popularity of these intelligent techniques is gaining popularity day by day among researchers from various disciplines of science and engineering. The sine cosine algorithm is a simple population-based stochastic approach for handling different optimization problems. In this work, we have discussed the basic sine cosine algorithm for continuous optimization problems, the multi-objective sine cosine algorithm for handling multi-objective optimization problems, and the discrete (or binary) versions of sine cosine algorithm for discrete optimization problems. Sine cosine algorithm (SCA) has reportedly shown competitive results when compared to other meta-heuristic algorithms. The easy implementation and less number of parameters make the SCA algorithm, a recommended choice for performing various optimization tasks. In this present chapter, we have studied different modifications and strategies for the advancement of the sine cosine algorithm. The incorporation of concepts like opposition-based learning, quantum simulation, and hybridization with other meta-heuristic algorithms have increased the efficiency and robustness of the SCA algorithm, and meanwhile, these techniques have also increased the application spectrum of the sine cosine algorithm.
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Boukhanovsky, Alexander V., and Sergey V. Ivanov. "Stochastic Simulation of Inhomogeneous Metocean Fields. Part III: High-Performance Parallel Algorithms." In Lecture Notes in Computer Science, 234–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44862-4_26.

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Budde, Carlos E., and Arnd Hartmanns. "Replicating $$\textsc {Restart}$$ with Prolonged Retrials: An Experimental Report." In Tools and Algorithms for the Construction and Analysis of Systems, 373–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_21.

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AbstractStatistical model checking uses Monte Carlo simulation to analyse stochastic formal models. It avoids state space explosion, but requires rare event simulation techniques to efficiently estimate very low probabilities. One such technique is $$\textsc {Restart}$$ R E S T A R T . Villén-Altamirano recently showed—by way of a theoretical study and ad-hoc implementation—that a generalisation of $$\textsc {Restart}$$ R E S T A R T to prolonged retrials offers improved performance. In this paper, we demonstrate our independent replication of the original experimental results. We implemented $$\textsc {Restart}$$ R E S T A R T with prolonged retrials in the and tools, and apply them to the models used originally. To do so, we had to resolve ambiguities in the original work, and refine our setup multiple times. We ultimately confirm the previous results, but our experience also highlights the need for precise documentation of experiments to enable replicability in computer science.
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Johnson, Erik A., Lawrence A. Bergman, David E. Goldberg, and Shirley J. Dyke. "Monte Carlo Simulation of Dynamical Systems of Engineering Interest in a Massively Parallel Computing Environment: an Application of Genetic Algorithms." In IUTAM Symposium on Advances in Nonlinear Stochastic Mechanics, 225–34. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0321-0_21.

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Qureshi, Sumaira Ejaz, and Roussos Dimitrakopoulos. "Comparison of Stochastic Simulation Algorithms in Mapping Spaces of Uncertainty of Non-linear Transfer Functions." In Geostatistics Banff 2004, 959–68. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/978-1-4020-3610-1_100.

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Palmisano, Alida, and Corrado Priami. "Stochastic Simulation Algorithm." In Encyclopedia of Systems Biology, 2009–10. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_768.

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Kuo, Chia-Tung, Da-Wei Wang, and Tsan-sheng Hsu. "Simple and Efficient Algorithms to Get a Finer Resolution in a Stochastic Discrete Time Agent-Based Simulation." In Advances in Intelligent Systems and Computing, 97–109. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03581-9_7.

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Conference papers on the topic "Stochastic simulation algorithms"

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Mohamed, Lina, Michael A. Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2009. http://dx.doi.org/10.2118/119139-ms.

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Hashemi, Fatemeh Sadat, and Raghu Pasupathy. "Averaging and derivative estimation within Stochastic Approximation algorithms." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465142.

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Ramaswamy, Rajesh, Ivo F. Sbalzarini, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Fast Exact Stochastic Simulation Algorithms Using Partial Propensities." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3497968.

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Köster, Till, and Adelinde M. Uhrmacher. "Handling Dynamic Sets of Reactions in Stochastic Simulation Algorithms." In SIGSIM-PADS '18: SIGSIM Principles of Advanced Discrete Simulation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3200921.3200943.

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Luboschik, Martin, Stefan Rybacki, Roland Ewald, Benjamin Schwarze, Heidrun Schumann, and Adelinde M. Uhrmacher. "Interactive visual exploration of simulator accuracy: A case study for stochastic simulation algorithms." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465190.

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Nie, Hao, and Qin Zhang. "Stochastic Simulation Method for Reasoning of Dynamic Uncertain Causality Graph (DUCG)." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16327.

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Abstract Dynamic Uncertain Causality Graph (DUCG) is an innovative model developed recently on the basis of dynamic causality diagram (DCD) model, which has been proved to be reliable for fault diagnosis of nuclear power plants. DUCG can represent complex uncertain causal relationship graphically, with both high efficient inference and support of incomplete expression. Therefore, DUCG is often built much larger than Bayesian Network (BN). However, as the scale of real problem is so large, DUCG still has the problem of combination explosion. Stochastic Simulation is a common solution for it. However, it is almost impossible to use traditional sampling algorithms for DUCG because the joint probability of evidences could be less than 10−20. In this paper, the algorithm based on conditional stochastic simulation for the inference of DUCG was proposed. It obtains the probability of evidences by calculating the expectation of the conditional probability in sampling process instead of using the sampling frequency, which overcomes the difficulty. What’s more, this algorithm uses recursive reasoning method of DUCG to calculate conditional probability distributions of node for sampling, which means this process only depends on its parent nodes’ states. As a result, the algorithm features in lower time complexity. In addition, it has the potential of parallelization like other sampling algorithms. In conclusion, this algorithm is promising to provide a new solution to the inference of the DUCG in large-scale and complex state situations.
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Mathesen, Logan, Giulia Pedrielli, and Szu Hui Ng. "Trust region based stochastic optimization with adaptive restart: A family of global optimization algorithms." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247943.

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Chen, Hongliang, and Xiaoping Li. "Periodic Solution for A Stochastic Non-autonomous Predator-prey Model with Crowley-Martin Function Response." In 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/mmsa-18.2018.11.

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Steuben, John C., and Cameron J. Turner. "The Impact of Asynchronous GPGPU Behaviors on Stochastic Simulation." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13221.

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This work examines the effect of one key aspect of General Purpose Graphics Processing Unit (GPGPU) computing on the realism and fidelity of stochastic simulations. In particular it is shown that the asynchronous nature of GPGPU computing can be leveraged to produce increased fidelity and realism, compared to conventional computing methods, when applied to probabilistic or stochastic simulations. This is a multifaceted argument that shows: 1) Asynchronous behaviors are essential to produce high computational throughput on GPGPU devices, and thus allow more rigorous sampling, which in turn enables a deeper understanding of the underlying stochastic processes. 2) Asynchronous GPGPU computing can eliminate the “global clock” present in simulations and potentially produce a better representation of the underlying process. This paper also attempts to give a working introduction to GPGPU computing, and to the applications of this technology in the field of stochastic simulation. A range of literature regarding these simulations is also surveyed, in order to provide context. A demonstration of synchronous versus asynchronous algorithms for robot swarm path planning is used to illustrate this discussion. Several notes on the limitations of GPGPU computing in this field are also made, along with remarks regarding future development of GPGPU-accelerated stochastic simulations.
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Levchenkov, Anatoly, and Mikhail Gorobetz. "Simulation of stochastic adaptive algorithms for embedded devices of railway safety systems." In 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE, 2015. http://dx.doi.org/10.1109/powereng.2015.7266354.

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Reports on the topic "Stochastic simulation algorithms"

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Bhatnagar, Shalabh, Michael C. Fu, Steven I. Marcus, and Shashank Bhatnagar. Randomized Difference Two-Timescale Simultaneous Perturbation Stochastic Approximation Algorithms for Simulation Optimization of Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada637176.

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Xiu, Dongbin. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1258292.

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Xiu, Dongbin. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1345533.

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Judd, Kenneth, Lilia Maliar, and Serguei Maliar. One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm. Cambridge, MA: National Bureau of Economic Research, January 2011. http://dx.doi.org/10.3386/w16708.

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