Academic literature on the topic 'Stochastic simulation technique (SST)'
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Journal articles on the topic "Stochastic simulation technique (SST)"
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Viseur, Sophie. "Turbidite reservoir characterization : object-based stochastic simulation meandering channels." Bulletin de la Société Géologique de France 175, no. 1 (January 1, 2004): 11–20. http://dx.doi.org/10.2113/175.1.11.
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Abstract Stochastic imaging has become an important tool for risk assessment and has successfully been applied to oil field management. This procedure aims at generating several possible and equiprobable 3D models of subsurface structures that enhance the available data set. Among these stochastic simulation techniques, object-based approaches consist of defining and distributing objects reproducing underground geobodies. A technical challenge still remains in object-based simulation. Due to advances in deep water drilling technology, new hydrocarbon exploration has been opened along the Atlantic margins. In these turbidite oil fields, segments of meandering channels can be observed on high-resolution seismic horizons. However, no present object-based simulation technique can reproduce exactly such known segments of channel. An improved object-based approach is proposed to simulate meandering turbidite channels conditioned on well observations and such seismic data. The only approaches dealing with meandering channels are process-based as opposed to structure-imitating. They are based on the reproduction of continental river evolution through time. Unfortunately, such process-based approaches cannot be used for stochastic imaging as they are based on equations reflecting meandering river processes and not turbiditic phenomena. Moreover, they incoporate neither shape constraints (such as channel dimensions and sinuosity) nor location constraints, such as well data. Last, these methods generally require hydraulic parameters that are not available from oil field study. The proposed approach aims at stochastically generating meandering channels with specified geometry that can be constrained to pass through well-observations. The method relies on the definition of geometrical parameters that characterize the shape of the expected channels such as dimensions, directions and sinuosity. The meandering channel object is modelled via a flexible parametric shape. The object is defined by a polygonal center-line (called backbone) that supports several sections. Channel sinuosity and local channel profiles are controlled by the backbone and, respectively the sections. Channel generation is performed within a 2D domain, D representing the channel-belt area. The proposed approach proceeds in two main steps. The first step consists in generating a channel center-line (C) defined by an equation v=Z(u) within the domain D. The geometry of this line is simulated using a geostatistical simulation technique that allows the generation of controlled but irregular center-lines conditioned on data points. During the second step, a vector field enabling the curve (C) to be transformed into a meandering curve (C’) is estimated. This vector field acts as a transform that specifies the third degree of channel sinuosity, in other words, the meandering parts of the loops. This field is parameterized by geometrical parameters such as curvature and tangent vectors along the curve (C) and the a priori maximum amplitude of the meander loops of the curve (C’). To make channel objects pass through conditioning points, adjustment vectors are computed at these locations and are interpolated along the curves. Synthetic datasets have been built to check if a priori parameters such as tortuosity are reproduced, and if the simulations are equiprobable. From this dataset, hundred simulations have been generated and enable one to verify that these two conditions are satisfied. Equiprobability is however not always satisfied from data points that are very close and located in a multivalued part of a meander : preferential orientation of the loops may indeed be observed. Solving this issue will be the focus of future works. Nevertheless, the results presented in this paper show that the approach provides satisfying simulations in any other configurations. This approach is moreover well-suited for petroleum reservoir characterization because it only needs specification of geometrical parameters such as dimension and sinuosity that can be inferred from the channel parts seen on seismic horizons or analogues.
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Rodionov, Alexander, Alexander Zhuchkov, and Victoria Pekut. "The Features of the Technique of Practical Training on “Fundamentals of Simulation of Automated Systems» Discipline." NBI Technologies, no. 1 (August 2019): 21–24. http://dx.doi.org/10.15688/nbit.jvolsu.2019.1.4.
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The paper deals with the content and methods of practical training in “Fundamentals of Simulation of Automated Systems”discipline. The relevance of the work is due to ever-growing requirements for the design of protected information systems, which are a class (subsystem) of automated systems. The level of mathematical training of students, and especially undergraduates, is significantly different. This leads to the need for the careful study of methods of lectures and especially practical training in the discipline. With multiple simulations of the network system, it is possible to accumulate statistics on the output scalars and thus compare the parameters of the system at different times. This is necessary because the network model is stochastic and depending on the initial values of the input data set by the random number generator, different simulation results can be obtained.
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Aragon-Calvo, M. A. "Smooth stochastic density field reconstruction." Monthly Notices of the Royal Astronomical Society 503, no. 1 (February 11, 2021): 557–62. http://dx.doi.org/10.1093/mnras/stab403.
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ABSTRACT We introduce a method for generating a continuous, mass-conserving and high-order differentiable density field from a discrete point distribution such as particles or haloes from an N-body simulation or galaxies from a spectroscopic survey. The method consists on generating an ensemble of point realizations by perturbing the original point set following the geometric constraints imposed by the Delaunay tessellation in the vicinity of each point in the set. By computing the mean field of the ensemble we are able to significantly reduce artefacts arising from the Delaunay tessellation in poorly sampled regions while conserving the features in the point distribution. Our implementation is based on the Delaunay Tessellation Field Estimation (DTFE) method; however, other tessellation techniques are possible. The method presented here shares the same advantages of the DTFE method such as self-adaptive scale, mass conservation, and continuity, while being able to reconstruct even the faintest structures of the point distribution usually dominated by artefacts in Delaunay-based methods. Additionally, we also present preliminary results of an application of this method to image denoising and artefact removal, highlighting the broad applicability of the technique introduced here.
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Carletti, Margherita, and Malay Banerjee. "A Backward Technique for Demographic Noise in Biological Ordinary Differential Equation Models." Mathematics 7, no. 12 (December 9, 2019): 1204. http://dx.doi.org/10.3390/math7121204.
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Physical systems described by deterministic differential equations represent idealized situations since they ignore stochastic effects. In the context of biomathematical modeling, we distinguish between environmental or extrinsic noise and demographic or intrinsic noise, for which it is assumed that the variation over time is due to demographic variation of two or more interacting populations (birth, death, immigration, and emigration). The modeling and simulation of demographic noise as a stochastic process affecting units of populations involved in the model is well known in the literature, resulting in discrete stochastic systems or, when the population sizes are large, in continuous stochastic ordinary differential equations and, if noise is ignored, in continuous ordinary differential equation models. The inverse process, i.e., inferring the effects of demographic noise on a natural system described by a set of ordinary differential equations, is still an issue to be addressed. With this paper, we provide a technique to model and simulate demographic noise going backward from a deterministic continuous differential system to its underlying discrete stochastic process, based on the framework of chemical kinetics, since demographic noise is nothing but the biological or ecological counterpart of intrinsic noise in genetic regulation. Our method can, thus, be applied to ordinary differential systems describing any kind of phenomena when intrinsic noise is of interest.
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Wilson, Spencer, Abdullah Alabdulkarim, and David Goldsman. "Green Simulation of Pandemic Disease Propagation." Symmetry 11, no. 4 (April 22, 2019): 580. http://dx.doi.org/10.3390/sym11040580.
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This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previous scenario. A set of Monte Carlo experiments illustrates the effectiveness of the procedure. We also propose various extensions of the method, including its use to estimate the sensitivity of propagation characteristics in response to small changes in the infection probability.
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CHAN, M. S., F. MUTAPI, M. E. J. WOOLHOUSE, and V. S. ISHAM. "Stochastic simulation and the detection of immunity to schistosome infections." Parasitology 120, no. 2 (February 2000): 161–69. http://dx.doi.org/10.1017/s003118209900534x.
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In this paper we address the question of detecting immunity to helminth infections from patterns of infection in endemic communities. We use stochastic simulations to investigate whether it would be possible to detect patterns predicted by theoretical models, using typical field data. Thus, our technique is to simulate a theoretical model, to generate the data that would be obtained in field surveys and then to analyse these data using methods usually employed for field data. The general behaviour of the model, and in particular the levels of variability of egg counts predicted, show that the model is capturing most of the variability present in field data. However, analysis of the data in detail suggests that detection of immunity patterns in real data may be very difficult even if the underlying patterns are present. Analysis of a real data set does show patterns consistent with acquired immunity and the implications of this are discussed.
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Yang, Hong-an, Yangyang Lv, Changkai Xia, Shudong Sun, and Honghao Wang. "Optimal Computing Budget Allocation for Ordinal Optimization in Solving Stochastic Job Shop Scheduling Problems." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/619254.
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We focus on solving Stochastic Job Shop Scheduling Problem (SJSSP) with random processing time to minimize the expected sum of earliness and tardiness costs of all jobs. To further enhance the efficiency of the simulation optimization technique of embedding Evolutionary Strategy in Ordinal Optimization (ESOO) which is based on Monte Carlo simulation, we embed Optimal Computing Budget Allocation (OCBA) technique into the exploration stage of ESOO to optimize the performance evaluation process by controlling the allocation of simulation times. However, while pursuing a good set of schedules, “super individuals,” which can absorb most of the given computation while others hardly get any simulation budget, may emerge according to the allocating equation of OCBA. Consequently, the schedules cannot be evaluated exactly, and thus the probability of correct selection (PCS) tends to be low. Therefore, we modify OCBA to balance the computation allocation: (1) set a threshold of simulation times to detect “super individuals” and (2) follow an exclusion mechanism to marginalize them. Finally, the proposed approach is applied to an SJSSP comprising 8 jobs on 8 machines with random processing time in truncated normal, uniform, and exponential distributions, respectively. The results demonstrate that our method outperforms the ESOO method by achieving better solutions.
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Cahen, Ewan Jacov, Michel Mandjes, and Bert Zwart. "RARE EVENT ANALYSIS AND EFFICIENT SIMULATION FOR A MULTI-DIMENSIONAL RUIN PROBLEM." Probability in the Engineering and Informational Sciences 31, no. 3 (January 23, 2017): 265–83. http://dx.doi.org/10.1017/s0269964816000553.
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This paper focuses on the evaluation of the probability that both components of a bivariate stochastic process ever simultaneously exceed some large level; a leading example is that of two Markov fluid queues driven by the same background process ever reaching the set (u, ∞)×(u, ∞), for u>0. Exact analysis being prohibitive, we resort to asymptotic techniques and efficient simulation, focusing on large values of u. The first contribution concerns various expressions for the decay rate of the probability of interest, which are valid under Gärtner–Ellis-type conditions. The second contribution is an importance-sampling-based rare-event simulation technique for the bivariate Markov modulated fluid model, which is capable of asymptotically efficiently estimating the probability of interest; the efficiency of this procedure is assessed in a series of numerical experiments.
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Ren, Y. J., I. Elishakoff, and M. Shinozuka. "Simulation of Multivariate Gaussian Fields Conditioned by Realizations of the Fields and Their Derivatives." Journal of Applied Mechanics 63, no. 3 (September 1, 1996): 758–65. http://dx.doi.org/10.1115/1.2823360.
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This paper investigates conditional simulation technique of multivariate Gaussian random fields by stochastic interpolation technique. For the first time in the literature a situation is studied when the random fields are conditioned not only by a set of realizations of the fields, but also by a set of realizations of their derivatives. The kriging estimate of multivariate Gaussian field is proposed, which takes into account both the random field as well as its derivative. Special conditions are imposed on the kriging estimate to determine the kriging weights. Basic formulation for simulation of conditioned multivariate random fields is established. As a particular case of uncorrelated components of multivariate field without realizations of the derivative of the random field, the present formulation includes that of univariate field given by Hoshiya. Examples of a univariate field and a three component field are elucidated and some numerical results are discussed. It is concluded that the information on the derivatives may significantly alter the results of the conditional simulation.
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Qi, Ji, and Yanhui Li. "L1 control for Itô stochastic nonlinear networked control systems." Transactions of the Institute of Measurement and Control 42, no. 14 (July 2, 2020): 2675–85. http://dx.doi.org/10.1177/0142331220923770.
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This paper investigates L1 control problem for a class of nonlinear stochastic networked control systems (NCSs) described by Takagi-Sugeno (T-S) fuzzy model. By exploiting a delay-dependent and basis-dependent Lyapunov-Krasovskii function and by means of the Itô stochastic differential equation technique, results on stability and L1 performance are proposed for the T-S fuzzy stochastic NCS. Specially, attention is focused on the fuzzy controller design that guarantees the closed-loop T-S fuzzy stochastic NCS is mean-square asymptotically stable and satisfies a prescribed L1 noise attenuation level [Formula: see text] with respect to all persistent and amplitude-bounded disturbance input signals. To reduce the conservatism of design, the signal transmission delay, data packet dropout, and quantization have been taken into consideration in the controller design. The corresponding design problem of L1 controller is converted into a convex optimization problem by solving a set of linear matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the feasibility and effectiveness of the proposed method.
Dissertations / Theses on the topic "Stochastic simulation technique (SST)"
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Tran-Canh, Dung. "Simulating the flow of some non-Newtonian fluids with neural-like networks and stochastic processes." University of Southern Queensland, Faculty of Engineering and Surveying, 2004. http://eprints.usq.edu.au/archive/00001518/.
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The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-layer perceptron networks (MLPNs) and radial basis function networks (RBFNs) to effectively eliminate the volume integrals in the integral formulation of fluid flow problems. An adaptive velocity gradient domain decomposition (AVGDD) scheme is incorporated into the computational algorithm. As a result, an improved feed forward neural network boundary-element-only method (FFNN- BEM) is created and verified. The present FFNN-BEM successfully simulates the flow of several Generalised Newtonian Fluids (GNFs), including the Carreau, Power-law and Cross models. To the best of the author's knowledge, the present FFNN-BEM is the first to achieve convergence for difficult flow situations when the power-law indices are very small (as small as 0.2). Although some elements are still used to discretise the governing equations, but only on the boundary of the analysis domain, the experience gained in the development of element-free approximation in the domain provides valuable skills for the progress towards an element-free approach. A least squares collocation RBFN-based mesh-free method is then developed for solving the governing PDEs. This method is coupled with the stochastic simulation technique (SST), forming the mesoscopic approach for analyzing viscoelastic flid flows. The velocity field is computed from the RBFN-based mesh-free method (macroscopic component) and the stress is determined by the SST (microscopic component). Thus the SST removes a limitation in traditional macroscopic approaches since closed form constitutive equations are not necessary in the SST. In this mesh-free method, each of the unknowns in the conservation equations is represented by a linear combination of weighted radial basis functions and hence the unknowns are converted from physical variables (e.g. velocity, stresses, etc) into network weights through the application of the general linear least squares principle and point collocation procedure. Depending on the type of RBFs used, a number of parameters will influence the performance of the method. These parameters include the centres in the case of thin plate spline RBFNs (TPS-RBFNs), and the centres and the widths in the case of multi-quadric RBFNs (MQ-RBFNs). A further improvement of the approach is achieved when the Eulerian SST is formulated via Brownian configuration fields (BCF) in place of the Lagrangian SST. The SST is made more efficient with the inclusion of the control variate variance reduction scheme, which allows for a reduction of the number of dumbbells used to model the fluid. A highly parallelised algorithm, at both macro and micro levels, incorporating a domain decomposition technique, is implemented to handle larger problems. The approach is verified and used to simulate the flow of several model dilute polymeric fluids (the Hookean, FENE and FENE-P models) in simple as well as non-trivial geometries, including shear flows (transient Couette, Poiseuille flows)), elongational flows (4:1 and 10:1 abrupt contraction flows) and lid-driven cavity flows.
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Ahn, Tae-Hyuk. "Computational Techniques for the Analysis of Large Scale Biological Systems." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77162.
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An accelerated pace of discovery in biological sciences is made possible by a new generation of computational biology and bioinformatics tools. In this dissertation we develop novel computational, analytical, and high performance simulation techniques for biological problems, with applications to the yeast cell division cycle, and to the RNA-Sequencing of the yellow fever mosquito.
Cell cycle system evolves stochastic effects when there are a small number of molecules react each other. Consequently, the stochastic effects of the cell cycle are important, and the evolution of cells is best described statistically. Stochastic simulation algorithm (SSA), the standard stochastic method for chemical kinetics, is often slow because it accounts for every individual reaction event. This work develops a stochastic version of a deterministic cell cycle model, in order to capture the stochastic aspects of the evolution of the budding yeast wild-type and mutant strain cells. In order to efficiently run large ensembles to compute statistics of cell evolution, the dissertation investigates parallel simulation strategies, and presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms. This work also proposes new accelerated stochastic simulation algorithms based on a fully implicit approach and on stochastic Taylor expansions.
Next Generation RNA-Sequencing, a high-throughput technology to sequence cDNA in order to get information about a sample's RNA content, is becoming an efficient genomic approach to uncover new genes and to study gene expression and alternative splicing. This dissertation develops efficient algorithms and strategies to find new genes in Aedes aegypti, which is the most important vector of dengue fever and yellow fever. We report the discovery of a large number of new gene transcripts, and the identification and characterization of genes that showed male-biased expression profiles. This basic information may open important avenues to control mosquito borne infectious diseases.Ph. D.
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Yilmaz, Bulent. "Stochastic Approach To Fusion Dynamics." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608517/index.pdf.
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This doctoral study consists of two parts. In the first part, the quantum statistical effects on the formation process of the heavy ion fusion reactions have been investigated by using the c-number quantum Langevin equation approach. It has been shown that the quantum effects enhance the over-passing probability at low temperatures. In the second part, we have developed a simulation technique for the quantum noises which can be approximated by two-term exponential colored noise.
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Birgoren, Gulum. "Strong motion simulation of the 1999 earthquakes in western Turkey : Stochastic Green's Function Technique with characterized source model and phase dependent site response." 京都大学 (Kyoto University), 2004. http://hdl.handle.net/2433/147832.
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Bufferand, Hugo. "Development of a fluid code for tokamak edge plasma simulation. Investigation on non-local transport." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4325/document.
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Pour concevoir les futurs réacteurs à fusion nucléaire, une bonne compréhension des mécanismes régissant l'intéraction plasma-paroi est requise. En particulier, il est nécessaire d'estimer quantitativement les flux de chaleurs impactant les matériaux et la contamination du coeur par les impuretés provenant du mur. Dans ce contexte, le code fluide SolEdge2D a été développé pour simuler le transport dans le plasma de bord. L'interaction plasma-paroi est prise en compte grâce à une méthode de pénalisation innovante et originale. Cette méthode permet en particulier de modéliser la géométrie complexe des éléments face au plasma avec une grande flexibilité. En parallèle, une étude plus théorique sur les propriétés du transport dans les milieux faiblement collisionels a été conduite avec les physiciens du groupe CSDC de l'université de Florence. Une généralisation de la loi de Fourier prenant en compte les corrélation spatio-temporelle à longue distance à été obtenue par l'analyse de modèles stochastiques 1D. Cette loi retrouve en particulier la transition entre un régime diffusif à forte collisionalté et un régime balistique à faible collisionalitéIn the scope of designing future nuclear fusion reactors, a clear understanding of the plasma-wall interaction is mandatory. Indeed, a predictive estimation of heat flux impacting the surface and the subsequent emission of impurities from the wall is necessary to ensure material integrity and energy confinement performances. In that perspective, the fluid code SolEdge2D has been developed to simulate plasma transport in the tokamak edge plasma. The plasma-wall interaction is modeled using an innovative penalization technique. This method enables in particular to take complex plasma facing components geometry into account. In parallel to this numerical effort, a theoretical work has been achieved to find appropriate corrections to fluid closures when collisionality drops. The study of stochastic 1D models has been realized in collaboration with physicists from the CSDC group in Florence. A generalized Fourier law taking long range spatio-temporal correlations has been found to properly account for ballistic transport in the low collisional regime. This formulation is expected to be used to model parallel heat flux or turbulent cross-field transport in tokamak plasmas
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Zhu, Wenjin. "Maintenance of monitored systems with multiple deterioration mechanisms in dynamic environments : application to wind turbines." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0005/document.
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Les travaux présentés contribuent à la modélisation stochastique de la maintenance de systèmes mono- ou multi-composants à détériorations et à modes de défaillances multiples en environnement dynamique. Dans ce cadre, les contributions portent d'une part sur la modélisation des processus de défaillance, et d'autre part sur la proposition de structures de décision de maintenance intégrant les différents types d'information de surveillance en ligne disponible sur le système (état de détérioration mesuré ou reconstruit, état de l'environnement, ...) et le développement des modèles mathématiques d'évaluation associés. Les modèles de détérioration et de défaillances proposés pour les systèmes mono-composants permettent de rendre compte de sources de détérioration multiples (chocs et détérioration graduelle) et d'intégrer les effets de l'environnement sur la dégradation. Pour les systèmes multi-composants, on insiste sur les risques concurrents, indépendants ou dépendants et sur l'intégration de l'environnement. Les modèles de maintenance développés sont adaptés aux modèles de détérioration proposés et permettent de prendre en compte la contribution de chaque source de détérioration dans la décision de maintenance, ou d'intégrer de l'information de surveillance indirecte dans la décision, ou encore de combiner plusieurs types d'actions de maintenance. Dans chaque cas, on montre comment les modèles développés répondent aux problématiques de la maintenance de turbines et de parcs éoliensThe thesis contributes to stochastic maintenance modeling of single or multi-components deteriorating systems with several failure modes evolving in a dynamic environment. In one hand, the failure process modeling is addressed and in the other hand, the thesis proposes maintenance decision rules taking into account available on-line monitoring information (system state, deterioration level, environmental conditions …) and develops mathematical models to measure the performances of the latter decision rules.In the framework of single component systems, the proposed deterioration and failure models take into account several deterioration causes (chocks and wear) and also the impact of environmental conditions on the deterioration. For multi-components systems, the competing risk models are considered and the dependencies and the impact of the environmental conditions are also studied. The proposed maintenance models are suitable for deterioration models and permit to consider different deterioration causes and to analyze the impact of the monitoring on the performances of the maintenance policies. For each case, the interest and applicability of models are analyzed through the example of wind turbine and wind turbine farm maintenance
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Toman, J. J., Z. Erdélyi, A. M. Gusak, M. Pasichnyy, V. Bezpalchuk, and B. Gajdics. "Stochastic Kinetic Mean Field model - a new, low-cost, atomic scale simulation technique." 2017. https://ul.qucosa.de/id/qucosa%3A31602.
Full textBook chapters on the topic "Stochastic simulation technique (SST)"
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Fujii, Tomohiro, and Masao Hirokawa. "A Data Concealing Technique with Random Noise Disturbance and a Restoring Technique for the Concealed Data by Stochastic Process Estimation." In International Symposium on Mathematics, Quantum Theory, and Cryptography, 103–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_11.
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Abstract We propose a technique to conceal data on a physical layer by disturbing them with some random noises, and moreover, a technique to restore the concealed data to the original ones by using the stochastic process estimation. Our concealing-restoring system manages the data on the physical layer from the data link layer. In addition to these proposals, we show the simulation result and some applications of our concealing-restoring technique.
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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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"The Source Simulation Technique (SST) source simulation technique (SST)." In Formulas of Acoustics, 1040–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76833-3_280.
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Kaur, Taranjit, and Balwinder Singh Dhaliwal. "Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique." In Applications of Artificial Intelligence in Electrical Engineering, 344–58. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2718-4.ch017.
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This chapter presents a mutation-based particle swarm optimization (PSO) approach for designing a linear phase digital low pass FIR filter (LPF). Since conventional gradient-based methods are susceptible to being trapped in local optima, the stochastic search methods have proven to be effective in a multi-dimensional non-linear environment. In this chapter, LPF with 20 coefficients has been designed. Since filter design is a multidimensional optimization problem, the concept of mutation helps in maintaining diversity in the swarm population and thereby efficiently controlling the local search and convergence to the global optimum solution. Given the filter specifications to be realized, the Mutation PSO (MPSO) tries to meet the ideal frequency response characteristics by generating an optimal set of filter coefficients. The simulation results have been compared with basic PSO and state of artworks on filter design. The results justify that the proposed technique outperforms not only in convergence speed but also in the quality of the solution obtained.
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Chen, E. Jack. "Simulation Output Analysis and Risk Management." In Analyzing Risk through Probabilistic Modeling in Operations Research, 200–220. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9458-3.ch009.
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Computer simulation is the process of designing and creating a computerized model of a real or proposed system for the purpose of conducting experiments to give us a better understanding of the behavior of the system under study for a given set of condition. Simulation studies have been used to investigate the characteristics of systems, to assess and analyze risks, for example, the probability of a machine breakdown. Hence, simulation is a valuable tool for risk management. However, estimates of measure of system performance from stochastic simulation are themselves random variables and are subject to sampling error. One must take into account sampling error when making inferences concerning system performance. We discuss how statistical techniques are applied in simulation output analysis, e.g., initialization bias reduction, tests of independence, confidence interval estimation, and quantile estimation. A carefully selected quantiles can reveals characteristic of the underlying distribution. These statistical techniques are key components of many simulation studies.
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Chainey, Spencer, and Neil Stuart. "Stochastic simulation: an alternative interpolation technique for digital geographic information." In Innovations In GIS 5, 3–24. CRC Press, 1998. http://dx.doi.org/10.1201/b16831-3.
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"Stochastic simulation: an alternative interpolation technique for digital geographic information." In Innovations In GIS 5, 25–46. CRC Press, 1998. http://dx.doi.org/10.1201/b16831-8.
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Pushnoi, Gr gor S., and Gordon L. Bonser. "Method of Systems Potential as "Top-Bottom" Technique of the Complex Adaptive Systems Modelling." In Intelligent Complex Adaptive Systems, 26–74. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-717-1.ch002.
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Emergent properties of complex adaptive systems (CAS) are explored by means of “agent-based modelling” (ABM), which are compared with results from modelling on the basis of the method of systems potential (MSP). MSP describes CAS as a holistic system whereas ABM-methodology considers CAS as set of interacting “agents.” It is argued that MSP is a “top-bottom” approach, which supplements ABM “bottom-up” modeling of CAS. Adaptive principles incorporated into CAS at the level of a holistic system exploit Lamarck’s ideas about evolution, while the adaptive rules incorporated in the inner structure of CAS reflect Darwin’s ideas. Both ABM and MSP exhibit the same macroscopic properties: (1) “punctuated equilibrium”; (2) sudden jumps in macro-indices; (3) cyclical dynamics; (4) superposition of deterministic and stochastic patterns in dynamics; (5) fractal properties of structure and dynamics; (6) SOC-phenomenon. ABM demonstrates these properties via simulations of the different models whereas MSP derives these phenomena analytically.
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Meitei, Ningombam Sanjib, and Snigdha Banerjee. "Application of Simulation Techniques." In Advances in Logistics, Operations, and Management Science, 268–99. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9888-8.ch014.
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In the present work, we provide a simulated inventory model incorporating multiple stochastic factors affecting an inventory model. This can provide solutions to managerial problems faced by retailers that have been addressed through the Single period problem (SPP) models. For a time dependent SPP with multiple discounts of random amounts at random time points, we consider a model wherein the factors demand rate, lead-time, number of discounts during a season, discount rates, time epoch at which a new discount rate is offered are stochastic. We provide solution procedures as pseudo algorithms for simulating near optimal order quantity and estimate of rate of price decline as well as optimal values of order quantity and total expected profit for a given value of initial selling price. Illustrative examples are presented in order to enable the researchers to be able to apply the methodology explained. The technique for estimating the probability that a business system shall be profitable or be a loss venture is demonstrated using numerical example.
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Kadry, Seifedine. "Stochastic Fatigue of a Mechanical System Using Random Transformation Technique." In Diagnostics and Prognostics of Engineering Systems, 182–88. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2095-7.ch009.
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In this chapter, a new technique is proposed to find the probability density function (pdf) of a stress for a stochastic mechanical system. This technique is based on the combination of the Probabilistic Transformation Method (PTM) and the Finite Element Method (FEM) to obtain the pdf of the response. The PTM has the advantage of evaluating the probability density function pdf of a function with random variable, by multiplying the joint density of the arguments by the Jacobien of the opposite function. Thus, the “exact” pdf can be obtained by using the probabilistic transformation method (PTM) coupled with the deterministic finite elements method (FEM). In the method of the probabilistic transformation, the pdf of the response can be obtained analytically when the pdf of the input random variables is known. An industrial application on a plate perforated with random entries was analyzed followed by a validation of the technique using the simulation of Monte Carlo.
Conference papers on the topic "Stochastic simulation technique (SST)"
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Ibrahim, Ilham H., and Constantin Chassapis. "Quantitative Assessment of the Risk of Variations During Medical Device Lifecycle." In ASME 2013 Conference on Frontiers in Medical Devices: Applications of Computer Modeling and Simulation. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/fmd2013-16109.
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The majority of medical devices are monitoring devices. Therefore, data communication and analysis are playing a crucial rule in predicting the effectiveness and reliability of a device. Device related data, patient related data and device-patient related data stored in Data Bases (DBs) are great sources for enhancing either new designs or improving already existing ones. Analyzing such data can provide researchers and device development teams with a complete justification and patterns of interest about a device’s performance, life and reliability. Data can be formulated into stochastic models based their statistical characteristics to consider the variability in data and the uncertainty about processes and procedures during early stages of the design process. This strengthens the device’s ability to function under a broader range of operating conditions. The work herein aims at targeting unwanted variations in device performance during the device development process. It employs a novel technique for variation risk management of device performance based historical process data modeling and visualization. The introduced technique is a proactive systematic procedure comprises a tool set that is being placed in the larger framework of the risk management procedure and fully utilizing data from the DBs to predict and address the risk of variations at the early stages of the design process rather than at the end of each major stage.
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Kumar, Apurva, A. J. Keane, P. B. Nair, and S. Shahpar. "Robust Design of Compressor Blades Against Manufacturing Variations." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99304.
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The aim of this paper is to develop and illustrate an efficient methodology to design blades with robust aerodynamic performance in the presence of manufacturing uncertainties. A novel geometry parametrization technique is developed to represent manufacturing variations due to tolerancing. A Gaussian Stochastic Process Model is trained using DOE techniques in conjunction with a high fidelity CFD solver. Bayesian Monte Carlo Simulation is then employed to obtain the statistics of the performance at each design point. A multiobjective optimizer is used to search the design space for robust designs. The multiobjective formulation allows explicit trade-off between the mean and variance of the performance. A design, selected from the robust design set is compared with a deterministic optimal design. The results demonstrate an effective method to obtain compressor blade designs which have reduced sensitivity to manufacturing variations with significant savings in computational effort.
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Li, Mingyang, and Zequn Wang. "LSTM-Based Ensemble Learning for Time-Dependent Reliability Analysis." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22006.
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Abstract This paper presents a long short-term memory (LSTM)-based ensemble learning framework for time-dependent reliability analysis. To deal with the time-dependent uncertainties, a LSTM network is first adopted to capture the system dynamics. As a result, time-dependent system responses for random realizations of stochastic processes can be accurately predicted by the LSTM. With realizations of the random variables and stochastic processes, multiple LSTMs are trained for generating a set of augmented data. Then a deep feedforward neural network (DFN) is employed to ensemble the knowledge extracted from LSTMs and generate a deep surrogate for the original time-dependent system responses. To improve the performance of DFN in terms of accuracy, the Gaussian process modeling technique is utilized for architecture design, where the number of neurons in the hidden layer is determined by minimizing the validation loss. With the DFN, the time-dependent system reliability can be directly approximated by using the Monte Carlo simulation. Two case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.
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Thornock, Niki C., Xiao-Hong Tu, and J. Kelly Flanagan. "A stochastic disk I/O simulation technique." In the 29th conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/268437.268743.
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Czerwinska, Justyna, and Nikolaus A. Adams. "Numerical Modeling of Micro-Channel Flows by a DPD Method." In ASME/JSME 2003 4th Joint Fluids Summer Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/fedsm2003-45127.
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This paper proposes new computational technique to model micro-flows. The presented below method is based on the meso-scale description of fluid. Dissipative Particle Dynamics (DPD) method is derived from Molecular Dynamics by means of coarse graining procedure. The dissipative particle is defined as a Voronoi cell with variable mass and size; evolves similarly to the Molecular Dynamics particles, except that inter-particle forces have additionally fluctuating, dissipative and stochastic component. This representation leads to the set of equations describing DPD approach. In this paper the outline of the DPD method for application to micro-fluidics flow is presented. DPD method in the form of Soft Fluid Particle model, was mainly applied in material science simulation. This paper presents new approach to model micro-flow by Voronoi Particle DPD method. As a particular example the gas flow in micro-channel flow is computed.
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Filip, Grzegorz P., Wenzhe Xu, and Kevin J. Maki. "Prediction of Extreme Wave Slamming Loads on a Fixed Platform." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78179.
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Design of offshore oil platforms requires accurate prediction of the maximum wave loads due to slamming on horizontal decks. The physical processes that influence the load are the propagation of irregular short-crested wind-driven storm seas, wave breaking, and wave-structure interaction. Furthermore, the ocean is a stochastic environment, so the load and its maximum can be considered as random variables. Ideally, the designer would like to know not only the most probable extreme load, but also the extreme load distribution. In this paper we will use a novel technique to prescribe wave environments that lead to extreme responses so that high-fidelity simulations of the highly-nonlinear process can be investigated in detail. Specifically, the dynamics of the relative motion of the sea surface and the platform will be assumed via the selection of a sea spectrum, and the extreme-value probability distribution function (PDF) will be calculated for a given exposure window. The novel aspect of the work is in the way that a set of deterministic sea environments will be generated that are amenable for simulation with a state-of-the-art computational-fluid dynamics (CFD) software. The resulting wave environments will be simulated to estimate the extreme-value PDF.
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Torres, Francisco, Emilio Sanchis, and Encarna Segarra. "Learning of stochastic dialog models through a dialog simulation technique." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-382.
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Carassale, Luigi, Michela Marrè-Brunenghi, and Stefano Patrone. "Modal Identification of Dynamically Coupled Bladed Disks in Run-Up Tests." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-57251.
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The spin test is a standard industrial practice employed for the qualification of rotor blades and disks. The expected results are the modal properties of blades and assemblages at different rotation velocities. If a significant dynamic coupling among the blades exists, global vibration modes appear, reflecting into a set of closely spaced natural frequencies for each mode family. In case of perfectly-tuned bladed disks, the circumferential structure of the mode shapes is known and can be exploited during the identification process so that traditional single-dof models may be applied. On the contrary, the mode irregularities produced by mistuning prevents the use of single-dof models requiring the development of more sophisticated approaches. In this work, we propose a multi-dof identification technique organized as follow: 1) the FRF of the bladed disk in the neighborhood of a resonance crossing is identified by the wavelet transform of the measured response; 2) the modal parameters of the system are estimated using a mixed stochastic-deterministic subspace algorithm formulated in the frequency domain. The procedure is validated using a realistic numerical simulation.
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Jimenez, Z., I. Azpiritxaga, and T. Lozada. "Stochastic Modeling Technique as Applied to History Matching of Eocene-Misoa Reservoir, Lake Maracaibo, Venezuela." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 1997. http://dx.doi.org/10.2118/38020-ms.
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HomChaudhuri, Baisravan. "Distributionally Robust Stochastic Model Predictive Control for Collision Avoidance." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9160.
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Abstract This paper focuses on distributionally robust controller design for avoiding dynamic and stochastic obstacles whose exact probability distribution is unknown. The true probability distribution of the disturbance associated with an obstacle, although unknown, is considered to belong to an ambiguity set that includes all the probability distributions that share the same first two moment. The controller thus focuses on ensuring the satisfaction of the probabilistic collision avoidance constraints for all probability distributions in the ambiguity set, hence making the solution robust to the true probability distribution of the stochastic obstacles. Techniques from robust optimization methods are used to model the distributionally robust probabilistic or chance constraints as a semi-definite programming (SDP) problem with linear matrix inequality (LMI) constraints that can be solved in a computationally tractable fashion. Simulation results for a robot obstacle avoidance problem shows the efficacy of our method.