Relevant bibliographies by topics / Stochastic simulator

Academic literature on the topic 'Stochastic simulator'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Stochastic simulator":

1

Chew Hernandez, Mario Luis, Leopoldo Viveros Rosas, and Jose Roberto Perez Torres. "A Stochastic Simulator of a Multi-Component Distillation Tower Built as an Excel Macro." Engineering, Technology & Applied Science Research 13, no. 2 (April 2, 2023): 10222–27. http://dx.doi.org/10.48084/etasr.5563.

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Dynamic process simulation is widely used in teaching controller design, as it allows foreseeing the performance of different control configurations and controller tunings. Currently, most college-level controller design exercises that are based on simulation consider deterministic perturbations (i.e. steps or ramps). In real life however, processes are more likely to face fluctuating, random disturbances, so the use of stochastic simulation in controller tuning exercises would provide students with an experience closer to their future professional practice than that provided by deterministic simulation. However, public institutions attempting to use dynamic, stochastic simulators in teaching, are hindered by the need of buying licenses of simulation packages or specialized programming languages (such as Matlab), as there aren´t any dynamic, stochastic simulators available as downloadable freeware. This paper shows a dynamic, stochastic simulator with a friendly interface of a distillation tower, developed as an Excel macro. This simulator has the advantage that it can be used at no cost to educational institutions since Excel is almost universally known and used by college faculties.
2

Köster, Till, Tom Warnke, and Adelinde M. Uhrmacher. "Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial Evaluation." ACM Transactions on Modeling and Computer Simulation 32, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3485465.

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Domain-specific modeling languages allow a clear separation between simulation model and simulator and, thus, facilitate the development of simulation models and add to the credibility of simulation results. Partial evaluation provides an effective means for efficiently executing models defined in such languages. However, it also implies some challenges of its own. We illustrate this and solutions based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement different approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. For the purpose of better understanding, we additionally generate intermediate variants, where only some parts are partially evaluated. Akin to profile-guided optimization, we use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to large biochemical reaction networks. The generic simulator achieves a performance similar to state-of-the-art simulators in the domain, whereas the specialized simulator outperforms established simulation tools with a speedup of more than an order of magnitude. Technical limitations in regard to the size of the generated code are discussed and overcome using a combination of link-time optimization and code separation. A detailed performance study is undertaken, investigating how and where partial evaluation has the largest effect.
3

Guan, Yongtao, and Stephen M. Krone. "WinSSS: Stochastic Spatial Simulator." Bulletin of the Ecological Society of America 85, no. 3 (July 2004): 102–4. http://dx.doi.org/10.1890/0012-9623(2004)85[102:wsss]2.0.co;2.

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4

Lee, Lung-fei. "INTERPOLATION, QUADRATURE, AND STOCHASTIC INTEGRATION." Econometric Theory 17, no. 5 (September 25, 2001): 933–61. http://dx.doi.org/10.1017/s0266466601175043.

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This paper considers features in numerical and stochastic integration approaches for the evaluation of analytically intractable integrals. It provides a unification of these two approaches. Some important features in quadrature formulations, namely, interpolation and region partition, can provide a valuable device for the design of a stochastic simulator. An interpolating function can be used as a valuable control variate for variance reduction in simulation. We illustrate possible variance reduction by some numerical cases with Gaussian quadrature. The resulting simulator may also be regarded as a monitor of the approximation error of a quadrature.
5

Xu, Jinghua, Kathleen L. Hancock, and Frank Southworth. "Simulation of Regional Freight Movement with Trade and Transportation Multinetworks." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 152–61. http://dx.doi.org/10.3141/1854-17.

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A simulation model called Trade and Transportation Multinetworks (TTMNet), constructed for the purpose of studying the effects of highly developed information technologies and logistic strategies (e.g., electronic commerce and real-time information) on freight transportation, is described. TTMNet is formulated as a multilevel product supply chain system that integrates the financial, informational, logistical, and physical aspects of transportation networks and allows interactions between each of these networks. Several simulators, including a freight traffic simulator, a supply chain decision-making simulator, and a pseudo-real-time information simulator, are involved. The freight traffic simulation is the focus of the present study. As part of this simulator, a learning model is set up to help decision makers estimate transportation costs on the basis of past experiences. Given the stochastic nature of these transportation costs and of the freight demands simulated by the system, the route for an origin–destination shipment may not remain optimal during a trip and may change along the way. A vehicle redirection procedure that handles this is presented. A numerical example is designed to compare a set of freight movements under two scenarios, one supported by and the other not supported by pseudo-real-time information on traffic conditions.
6

Amar, Patrick. "Pandæsim: An Epidemic Spreading Stochastic Simulator." Biology 9, no. 9 (September 18, 2020): 299. http://dx.doi.org/10.3390/biology9090299.

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Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie stochastic algorithm, along with the sub-volumes method to take into account the spatial localisation. Our algorithm allows us to easily switch from stochastic discrete simulation to continuous deterministic resolution using mean values. We applied our approaches on the study of the Covid-19 epidemic in France. The stochastic discrete version of Pandæsim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. Moreover, we have highlighted interesting differences in behaviour between the continuous and discrete methods that may arise in some particular conditions.
7

Marchetti, Luca, Rosario Lombardo, and Corrado Priami. "HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1232868.

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HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA). HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA).
8

Braun, Willard J. "Assessing a Stochastic Fire Spread Simulator." Journal of Environmental Informatics 22, no. 1 (September 25, 2013): 1–12. http://dx.doi.org/10.3808/jei.201300241.

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Ribeiro, A. S., D. A. Charlebois, and J. Lloyd-Price. "CellLine, a stochastic cell lineage simulator." Bioinformatics 23, no. 24 (October 9, 2007): 3409–11. http://dx.doi.org/10.1093/bioinformatics/btm491.

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Chen, Yixing, Tianzhen Hong, and Xuan Luo. "An agent-based stochastic Occupancy Simulator." Building Simulation 11, no. 1 (June 1, 2017): 37–49. http://dx.doi.org/10.1007/s12273-017-0379-7.

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You might also be interested in the extended bibliographies on the topic 'Stochastic simulator' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Stochastic simulator":

1

Chua, Cheong Wei 1975. "A stochastic pool-based electricity market simulator /." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31045.

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In Part I, two pool-based electricity market models are compared in terms of their economic impact on the market participants, the Lossless Economic Dispatch (LED) and the Optimal Power Flow (OPF). The OPF is shown to be economically more efficient, more accurate and more equitable to the participants.
In Part II, a stochastic electricity market simulator (SEMS) is designed using elements of Monte Carlo methods and game theory. Each generator is assumed to operate in a stochastic manner, according to a bid strategy composed of a set of pre-established bid instances and a corresponding set of bid probabilities. The Pool dispatches power and defines prices according to either the LED or OPF models from Part I. Generators can update their bidding strategies according to a profit performance index reflecting their degree of risk tolerance, Chicken (risk averse), Average, and Cowboy (risk taker). SEMS can predict issues such as unintended collusion, as well as to evaluate bidding strategies.
2

Kim, Daniel D. 1982. "A biological simulator using a stochastic approach for synthetic biology." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33307.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 58-59).
Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell.
by Daniel D. Kim.
M.Eng.
3

Fan, Futing. "Improving GEMFsim: a stochastic simulator for the generalized epidemic modeling framework." Kansas State University, 2016. http://hdl.handle.net/2097/34564.

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Master of Science
Department of Electrical and Computer Engineering
Caterina M. Scoglio
The generalized epidemic modeling framework simulator (GEMFsim) is a tool designed by Dr. Faryad Sahneh, former PhD student in the NetSE group. GEMFsim simulates stochastic spreading process over complex networks. It was first introduced in Dr. Sahneh’s doctoral dissertation "Spreading processes over multilayer and interconnected networks" and implemented in Matlab. As limited by Matlab language, this implementation typically solves only small networks; the slow simulation speed is unable to generate enough results in reasonable time for large networks. As a generalized tool, this framework must be equipped to handle large networks and contain sufficient support to provide adequate performance. The C language, a low-level language that effectively maps a program to machine in- structions with efficient execution, was selected for this study. Following implementation of GEMFsim in C, I packed it into Python and R libraries, allowing users to enjoy the flexibility of these interpreted languages without sacrificing performance. GEMFsim limitations are not limited to language, however. In the original algorithm (Gillespie’s Direct Method), the performance (simulation speed) is inversely proportional to network size, resulting in unacceptable speed for very large networks. Therefore, this study applied the Next Reaction Method, making the performance irrelevant of network size. As long as the network fits into memory, the speed is proportional to the average node degree of the network, which is not very large for most real-world networks. This study also applied parallel computing in order to advantageously utilize multiple cores for repeated simulations. Although single simulation can not be paralleled as a Markov process, multiple simulations with identical network structures were run simultaneously, sharing one network description in memory.
4

Boulianne, Laurier. "An algorithm and VLSI architecture for a stochastic particle based biological simulator." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96690.

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With the recent progress in both computer technology and systems biology, it is now possible to simulate and visualise biological systems virtually. It is expected that realistic in silico simulations will enhance our understanding of biological processes and will promote the development of effective therapeutic treatments. Realistic biochemical simulators aim to improve our understanding of biological processes that could not be, otherwise, properly understood in experimental studies. This situation calls for increasingly accurate simulators that take into account not only the stochastic nature of biological systems, but also the spatial heterogeneity and the effect of crowding of biological systems. This thesis presents a novel particle-based stochastic biological simulator named Grid- Cell. It also presents a novel VLSI architecture accelerating GridCell between one and two orders of magnitude. GridCell is a three-dimensional simulation environment for investigating the behaviour of biochemical networks under a variety of spatial influences including crowding, recruitment and localisation. GridCell enables the tracking and characterisation of individual particles, leading to insights on the behaviour of low copy number molecules participating in signalling networks. The simulation space is divided into a discrete 3D grid that provides ideal support for particle collisions without distance calculations and particle searches. SBML support enables existing networks to be simulated and visualised. The user interface provides intuitive navigation that facilitates insights into species behaviour across spatial and temporal dimensions. Crowding effects on a Michaelis- Menten system are simulated and results show they can have a huge impact on the effective rate of product formation. Tracking millions of particles is extremely computationally expensive and in order to run whole cells at the molecular resolution in less than 24 hours, a commonly expressed goal in systems biology, accelerating GridCell with parallel hardware is required. An FPGA architecture combining pipelining, parallel processing units and streaming is presented. The architecture is scalable to multiple FPGAs and the streaming approach ensures that the architecture scales well to very large systems. An architecture containing 25 processing units on each stage of the pipeline is synthesised on a single Virtex-6 XC6VLX760 FPGA device and a speedup of 76x over the serial implementation is achieved. This speedup reduces the gap between the complexity of cell simulation and the processing power of advanced simulators. Future work on GridCell could include support for highly complex compartment and high definition particles.
Grâce aux récents progrès en informatique et en biologie, il est maintenant possible de simuler et de visualiser des systèmes biologiques de façon virtuelle. Il est attendu que des simulations réalistes produites par ordinateur, in silico, nous permettront d'améliorer notre connaissance des processus biologiques et de favoriser le développement de traitements thérapeutiques efficaces. Les simulateurs biologiques visent à améliorer notre connaissance de processus biologiques qui, autrement, ne pourraient pas être correctement analysés par des études expérimentales. Cette situation requiert le développement de simulateurs de plus en plus précis qui tiennent compte non seulement de la nature stochastique des systèmes biologiques, mais aussi de l'hétérogénéité spatiale ainsi que des effets causés par la grande densité de particules présentes dans ces systèmes. Ce mémoire présente GridCell, un simulateur biologique stochastique original basé sur une représentation microscopique des particules. Ce mémoire présente aussi une architecture parallèle originale accélérant GridCell par presque deux ordres de magnitude. GridCell est un environnement de simulation tridimensionnel qui permet d'étudier le comportement des réseaux biochimique sous différentes influences spatiales, notamment l'encombrement moléculaire ainsi que les effets de recrutement et de localisation des particules. GridCell traque les particules individuellement, ce qui permet d'explorer le comportement de molécules participants en très petits nombres à divers réseaux de signalisation. L'espace de simulation est divisé en une grille 3D discrète qui permet de générer des collisions entre les particules sans avoir à faire de calculs de distance ni de recherches de particules complexes. La compatibilité avec le format SBML permet à des réseaux déjà existants d'être simulés et visualisés. L'interface visuelle permet à l'utilisateur de naviguer de façon intuitive dans la simulation afin d'observer le comportement des espèces à travers le temps et l'espace. Des effets d'encombrement moléculaire sur un système enzymatique de type Michaelis-Menten sont simulés, et les résultats montrent un effet important sur le taux de formation du produit. Tenir compte de millions de particules à la fois est extrêmement demandant pour un ordinateur et, pour pouvoir simuler des cellules complètes avec une résolution spatiale moléculaire en moins d'une journée, un but souvent exprimé en biologie des systèmes, il est essentiel d'accélérer GridCell à l'aide de matériel informatique fonctionnant en parallèle. On propose une architecture sur FPGA combinant le traitement en pipeline, le fonctionnement en mode continu ainsi que l'exécution parallèle. L'architecture peut supporter plusieurs FPGA et l'approche en mode continu permet à l'architecture de supporter très grands systèmes. Une architecture comprenant 25 unités de traitement sur chaque étage du pipeline est synthétisée sur un seul FPGA Virtex-6 XC6VLX760, ce qui permet d'obtenir des gains de performance 76 fois supérieurs à l'implémentation séquentielle de l'algorithme. Ce gain de performance réduit l'écart entre la complexité de la simulation des cellules biologiques et la puissance de calcul des simulateurs avancés. Des travaux futurs sur GridCell pourraient avoir pour objectif de supporter des compartiments de forme très complexe ainsi que des particules haute définition.
5

Soltani-Moghaddam, Alireza. "Network simulator design with extended object model and generalized stochastic petri-net /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999317.

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6

Taleb, B. "The theory and design of a stochastic reliability simulator for large scale systems." Thesis, Open University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383689.

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7

PINTO, ROBERTO JOSE. "STOCHASTIC SIMULATOR TO CALCULATE THE AGENTS FINANCIAL FLOW AT BRAZILIAN WHOLESALE ENERGY MARKET." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2876@1.

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ELETROBRAS - CENTRAIS ELÉTRICAS BRASILEIRAS S. A.
No novo modelo de livre concorrência do Setor Elétrico Nacional,o Mercado Atacadista de Energia (MAE) foi criado para ser o ambiente onde se processam as compras e vendas de energia de curto prazo. Logo, os agentes que possuem excedentes de energia, provenientes de excesso de geração ou de sobra de contrato, poderão vendê-los no MAE. A situação inversa também pode ocorrer, ou seja, o agente que necessitar de energia para cobrir um deficit de energia ou honrar contratos também poderá comprar energia no MAE. Em cada instante de tempo, os montantes de energia que cada agente poderá comercializar no MAE, assim como o preço de liquidação, não podem ser previstos com exatidão, pois dependem, por exemplo, das condições hidrológicas futuras. Isto acarreta incertezas com relação ao fluxo de caixa futuro dos agentes.No presente trabalho é apresentado um modelo de simulador estocástico capaz de fornecer estimativas futuras do fluxo financeiro de um agente no MAE, considerando-se em detalhe as regras vigentes, analisando- se diversos cenários hidrológicos.
In the new trading model for the Brazilian electricity sector, the Wholesale Energy Market -Mercado Atacadista de Energia - MAE- is the place where all buyers and sellers of electricity can trade and in which the spot price of energy will be determined. In this market the agents can sell the excess of generation or the positive net energy of bilateral contracts. However, lack of generation or negative net energy of bilateral contracts will be exposured to spot market price.The market price and the energy amount that each agent can trade at MAE depends on many factors, such as future hydrological conditions, for example.This fact causes financial flow uncertainties to all market agents. Then, this dissertation shows a model to make the market accounts using the MAE rules and future estimation of generations and consumptions energies. The results of this model could help the agents to forecast the payments and receipts at MAE.
8

Olsén, Jörgen. "Stochastic Modeling and Simulation of the TCP protocol." Doctoral thesis, Uppsala University, Mathematical Statistics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3534.

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The success of the current Internet relies to a large extent on a cooperation between the users and the network. The network signals its current state to the users by marking or dropping packets. The users then strive to maximize the sending rate without causing network congestion. To achieve this, the users implement a flow-control algorithm that controls the rate at which data packets are sent into the Internet. More specifically, the Transmission Control Protocol (TCP) is used by the users to adjust the sending rate in response to changing network conditions. TCP uses the observation of packet loss events and estimates of the round trip time (RTT) to adjust its sending rate.

In this thesis we investigate and propose stochastic models for TCP. The models are used to estimate network performance like throughput, link utilization, and packet loss rate. The first part of the thesis introduces the TCP protocol and contains an extensive TCP modeling survey that summarizes the most important TCP modeling work. Reviewed models are categorized as renewal theory models, fixed-point methods, fluid models, processor sharing models or control theoretic models. The merits of respective category is discussed and guidelines for which framework to use for future TCP modeling is given.

The second part of the thesis contains six papers on TCP modeling. Within the renewal theory framework we propose single source TCP-Tahoe and TCP-NewReno models. We investigate the performance of these protocols in both a DropTail and a RED queuing environment. The aspects of TCP performance that are inherently depending on the actual implementation of the flow-control algorithm are singled out from what depends on the queuing environment.

Using the fixed-point framework, we propose models that estimate packet loss rate and link utilization for a network with multiple TCP-Vegas, TCP-SACK and TCP-Reno on/off sources. The TCP-Vegas model is novel and is the first model capable of estimating the network's operating point for TCP-Vegas sources sending on/off traffic. All TCP and network models in the contributed research papers are validated via simulations with the network simulator ns-2.

This thesis serves both as an introduction to TCP and as an extensive orientation about state of the art stochastic TCP models.

9

Erben, Vojtěch. "Návrh a testování stochastické navigace v TRASI." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219900.

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The thesis deals with the design and implementation of routing algorithms in trafic simulator TRASI. These algorithms are capable of planning vehicle's route by giving a set of crossroads that vehicle needs to go through. Furthermore, this work deals with design and implementation of stochastic navigation including implementation of communication between vehicles. Stochastic navigation suggests several alternative routes based on a traffic event. From these routes is randomly (stochastically) choosen one based on information about the throughput of particular found routes. In the introduction of this work is described the traffic simulator TRASI, it's user interface and basic control interface. Further is described theory of traffic flow on macroscopic and microscopic level, followed by the descripion of algorithms for oriented graphs traversal and their implementation in the simulator. In the following parts of this thesis is described communication layer, that takes care of the communication between vehicles, and it's implementation. Further is described design and implementation of stochastic navigation. In the final chapter is done verification of the functionality of the simulator and tests of particular routing algorithms.
10

Echavarria, Gregory Maria Angelica. "Predictive Data-Derived Bayesian Statistic-Transport Model and Simulator of Sunken Oil Mass." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/471.

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Sunken oil is difficult to locate because remote sensing techniques cannot as yet provide views of sunken oil over large areas. Moreover, the oil may re-suspend and sink with changes in salinity, sediment load, and temperature, making deterministic fate models difficult to deploy and calibrate when even the presence of sunken oil is difficult to assess. For these reasons, together with the expense of field data collection, there is a need for a statistical technique integrating limited data collection with stochastic transport modeling. Predictive Bayesian modeling techniques have been developed and demonstrated for exploiting limited information for decision support in many other applications. These techniques brought to a multi-modal Lagrangian modeling framework, representing a near-real time approach to locating and tracking sunken oil driven by intrinsic physical properties of field data collected following a spill after oil has begun collecting on a relatively flat bay bottom. Methods include (1) development of the conceptual predictive Bayesian model and multi-modal Gaussian computational approach based on theory and literature review; (2) development of an object-oriented programming and combinatorial structure capable of managing data, integration and computation over an uncertain and highly dimensional parameter space; (3) creating a new bi-dimensional approach of the method of images to account for curved shoreline boundaries; (4) confirmation of model capability for locating sunken oil patches using available (partial) real field data and capability for temporal projections near curved boundaries using simulated field data; and (5) development of a stand-alone open-source computer application with graphical user interface capable of calibrating instantaneous oil spill scenarios, obtaining sets maps of relative probability profiles at different prediction times and user-selected geographic areas and resolution, and capable of performing post-processing tasks proper of a basic GIS-like software. The result is a predictive Bayesian multi-modal Gaussian model, SOSim (Sunken Oil Simulator) Version 1.0rc1, operational for use with limited, randomly-sampled, available subjective and numeric data on sunken oil concentrations and locations in relatively flat-bottomed bays. The SOSim model represents a new approach, coupling a Lagrangian modeling technique with predictive Bayesian capability for computing unconditional probabilities of mass as a function of space and time. The approach addresses the current need to rapidly deploy modeling capability without readily accessible information on ocean bottom currents. Contributions include (1) the development of the apparently first pollutant transport model for computing unconditional relative probabilities of pollutant location as a function of time based on limited available field data alone; (2) development of a numerical method of computing concentration profiles subject to curved, continuous or discontinuous boundary conditions; (3) development combinatorial algorithms to compute unconditional multimodal Gaussian probabilities not amenable to analytical or Markov-Chain Monte Carlo integration due to high dimensionality; and (4) the development of software modules, including a core module containing the developed Bayesian functions, a wrapping graphical user interface, a processing and operating interface, and the necessary programming components that lead to an open-source, stand-alone, executable computer application (SOSim - Sunken Oil Simulator). Extensions and refinements are recommended, including the addition of capability for accepting available information on bathymetry and maybe bottom currents as Bayesian prior information, the creation of capability of modeling continuous oil releases, and the extension to tracking of suspended oil (3-D).
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Books on the topic "Stochastic simulator":

1

Canada. Dept. of the Environment. National Hydrology Research Institute. A numerical simulator for flow and transport in stochastic discrete fracture networks. S.l: s.n, 1988.

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Directorate, Canada Inland Waters. A numerical simulator for flow and transport in stochastic discrete fracture networks. Saskatoon, Sask: Inland Waters Directorate, National Hydrology Research Institute, National Hydrology Research Centre, 1988.

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Greboval, Dominique Franc ʹois. Fisheries management under stochastic conditions: A bioeconomic simulator of the New England groundfishfishery. Ann Arbor, Mich: University Microfilms International, 1988.

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Kelley, Neil D. Turbulence-turbine interaction: The basis for the development of the TurbSim Stochastic Simulator. Golden, CO: National Renewable Energy Laboratory, 2011.

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Ripley, Brian D. Stochastic simulation. New York: Wiley, 1987.

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Ripley, Brian D., ed. Stochastic Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/9780470316726.

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Shedler, G. S. Regenerative stochastic simulation. Boston: Academic Press, 1993.

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sentralbyrå, Norway Statistisk, ed. Stochastic simulation of KVARTS91. Oslo: Statistisk sentralbyrå, 1993.

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MacKeown, P. K. Stochastic simulation in physics. New York: Springer, 1997.

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Nelson, Barry L. Stochastic modeling: Analysis & simulation. Mineloa, N.Y: Dover Publications, 2002.

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Book chapters on the topic "Stochastic simulator":

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Segovia-Hernández, Juan Gabriel, and Fernando Israel Gómez-Castro. "The Simulator Aspen Plus®." In Stochastic Process Optimization using Aspen Plus®, 55–59. Boca Raton : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155739-4.

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Cannon, Robert. "PSICS: the Parallel Stochastic Ion Channel Simulator." In Encyclopedia of Computational Neuroscience, 2531–32. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_260.

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Cannon, Robert. "PSICS: The Parallel Stochastic Ion Channel Simulator." In Encyclopedia of Computational Neuroscience, 1–2. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_260-1.

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Cannon, Robert. "PSICS: The Parallel Stochastic Ion Channel Simulator." In Encyclopedia of Computational Neuroscience, 1–2. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_260-2.

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Santner, Thomas J., Brian J. Williams, and William I. Notz. "Stochastic Process Models for Describing Computer Simulator Output." In Springer Series in Statistics, 27–66. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8847-1_2.

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Köppen, Veit, Marina Allgeier, and Hans-J. Lenz. "Balanced Scorecard Simulator — A Tool for Stochastic Business Figures." In Studies in Classification, Data Analysis, and Knowledge Organization, 457–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_52.

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Andrews, Steven S. "Spatial and Stochastic Cellular Modeling with the Smoldyn Simulator." In Bacterial Molecular Networks, 519–42. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-61779-361-5_26.

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Montagna, Sara, and Andrea Roli. "Parameter Tuning of a Stochastic Biological Simulator by Metaheuristics." In AI*IA 2009: Emergent Perspectives in Artificial Intelligence, 466–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10291-2_47.

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Bernardeschi, Cinzia, Andrea Domenici, and Maurizio Palmieri. "Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator." In Software Technologies: Applications and Foundations, 34–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04771-9_3.

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Yamamoto, O., Yuichiro Shibata, Hitoshi Kurosawa, and Hideharu Amano. "A Reconfigurable Stochastic Model Simulator for Analysis of Parallel Systems." In Lecture Notes in Computer Science, 475–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44614-1_52.

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Conference papers on the topic "Stochastic simulator":

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Thornburg, Jesse, Bruce Krogh, and Taha Selim Ustun. "Stochastic Simulator for Smart Microgrid Planning." In ACM DEV '16: Annual Symposium on Computing for Development. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3001913.3006631.

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Theofanis, Patrick L., and Oleg Tazetdinov. "Monte Carlo EUV stochastic simulator (MESS): a chemistry-oriented lithography simulator." In Optical and EUV Nanolithography XXXV, edited by Anna Lio and Martin Burkhardt. SPIE, 2022. http://dx.doi.org/10.1117/12.2617292.

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Alemany, Kristina, and John Olds. "LASSO - Lunar Architecture Stochastic Simulator and Optimizer." In AIAA Modeling and Simulation Technologies Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-6415.

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Xue, Xinfeng, Liang Zheng, and Jin Ye. "A Stochastic Simulation-Based Optimization Method for Calibrating VISSIM Simulator under Uncertainties." In 19th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2019. http://dx.doi.org/10.1061/9780784482292.260.

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Asadi, Hirad, and Johan Schubert. "A stochastic discrete event simulator for effects-based planning." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721654.

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Skrebtsov, Andrey, Zijian Bai, Guido H. Bruck, and Peter Jung. "A novel network simulator based on stochastic spatial models." In 2013 7th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2013. http://dx.doi.org/10.1109/icspcs.2013.6723927.

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Xue, Mengran, Sandip Roy, Stephen Zobell, Yan Wan, Christine Taylor, and Craig Wanke. "A Stochastic Spatiotemporal Weather-Impact Simulator: Representative Scenario Selection." In 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-6812.

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Lecca, Paola, Lorenzo Dematte, Adaoha E. C. Ihekwaba, and Corrado Priami. "Redi: A Simulator of Stochastic Biochemical Reaction-Diffusion Systems." In 2010 Second International Conference on Advances in System Simulation (SIMUL). IEEE, 2010. http://dx.doi.org/10.1109/simul.2010.14.

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Varol, Huseyin Atakan. "MOSES: A Matlab-based open-source stochastic epidemic simulator." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591271.

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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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Reports on the topic "Stochastic simulator":

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Ringhand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai, and Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.

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Abstract:
This document is intended to give an overview of the human subject study in a driving simulator that was conducted by the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD) to provide the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD with the necessary input for the validation of a stochastic traffic simulation, especially for the parameterization, consolidation, and validation of driver behaviour models. VPSY planned, conducted, and analysed a driving simulator study. The main purpose of the study was to analyse driving behaviour and gaze data at intersections in urban areas. Based on relevant literature, a simulated driving environment was created, in which a sample of drivers passed a variety of intersections. Considering different driver states, driving tasks, and traffic situations, the collected data provide detailed information about human gaze and driving behaviour when approaching and crossing intersections. The collected data was transferred to LKT for the development of the stochastic traffic simulation.
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Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.

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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behaviour of the study participants and the driving behaviour of the agents in the traffic simulation with real world data. Based on relevant literature, a validation concept was designed and real world data was collected using drones and stationary cameras. By means of qualitative and quantitative analysis it could be shown, that the driving simulator study shows realistic driving behaviour in terms of mean speed. Moreover, the stochastic traffic simulation already reflects reality in terms of mean and maximum speed of the agents. Finally, the performed evaluation proofed the suitability of the developed stochastic simulation for the assessment process. Furthermore, it could be shown, that a C-AEB system improves the traffic safety for the chosen test-scenarios.
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Kelley, N. D., and B. J. Jonkman. Overview of the TurbSim Stochastic Inflow Turbulence Simulator. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/15020329.

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Frazier, John, Yaroslav Chusak, and Brent Foy. Stochastic Simulation of Biomolecular Reaction Networks Using the Biomolecular Network Simulator Software. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada484775.

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Kelley, N. D., and B. J. Jonkman. Overview of the TurbSim Stochastic Inflow Turbulence Simulator: Version 1.10. Office of Scientific and Technical Information (OSTI), September 2006. http://dx.doi.org/10.2172/891590.

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Kelley, Neil D. Turbulence-Turbine Interaction: The Basis for the Development of the TurbSim Stochastic Simulator. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1031981.

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Kelley, N. D., and B. J. Jonkman. Overview of the TurbSim Stochastic Inflow Turbulence Simulator: Version 1.21 (Revised February 1, 2001). Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/903073.

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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod, and Günther Prokop. Report on integration of the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.246.

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As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.
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James Glimm and Xiaolin Li. Multiscale Stochastic Simulation and Modeling. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/862194.

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Field, Richard V. ,. Jr. Stochastic models: theory and simulation. Office of Scientific and Technical Information (OSTI), March 2008. http://dx.doi.org/10.2172/932886.

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