Journal articles on the topic 'Stochastic simulator'

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.

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.

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.

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.

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.

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).

Abstract A method for the stochastic simulation of (rain)drop size distributions (DSDs) in space and time using geostatistics is presented. At each pixel, the raindrop size distribution is described by a Gamma distribution with two or three stochastic parameters. The presence or absence of rainfall is modeled using an indicator field. Separable space–time variograms are used to estimate and reproduce the spatial and temporal structures of all these parameters. A simple and user-oriented procedure for the parameterization of the simulator is proposed. The only data required are DSD time series and radar rain-rate (or reflectivity) measurements. The proposed simulation method is illustrated for both frontal and convective precipitation using real data collected in the vicinity of Lausanne, Switzerland. The spatial and temporal structures of the simulated fields are evaluated and validated using DSD measurements from eight independent disdrometers.

Summary For large- scale history- matching problems, optimization algorithms which require only the gradient of the objective function and avoid explicit computation of the Hessian appear to be the best approach. Unfortunately, such algorithms have not been extensively used in practice because computation of the gradient of the objective function by the adjoint method requires explicit knowledge of the simulator numerics and expertise in simulation development. Here we apply the simultaneous perturbation stochastic approximation (SPSA) method to history match multiphase flow production data. SPSA, which has recently attracted considerable international attention in a variety of disciplines, can be easily combined with any reservoir simulator to do automatic history matching. The SPSA method uses stochastic simultaneous perturbation of all parameters to generate a down hill search direction at each iteration. The theoretical basis for this probabilistic perturbation is that the expectation of the search direction generated is the steepest descent direction. We present modifications for improvement in the convergence behavior of the SPSA algorithm for history matching and compare its performance to the steepest descent, gradual deformation and LBFGS algorithm. Although the convergence properties of the SPSA algorithm are not nearly as good as our most recent implementation of a quasi-Newton method (LBFGS), the SPSA algorithm is not simulator specific and it requires only a few hours of work to combine SPSA with any commercial reservoir simulator to do automatic history matching. To the best of our knowledge, this is the first introduction of SPSA into the history matching literature. Thus, we make considerable effort to put it in a proper context.

Simulation and analysis of patient flow can contribute to the safe and efficient functioning of a healthcare system, yet it is rarely incorporated into routine healthcare management, partially due to the technical training required. This paper introduces a free and open source patient flow simulation software tool that enables training and experimentation with healthcare management decisions and their impact on patient flow. Users manage their simulated hospital with a simple web-based graphical interface. The model is a stochastic discrete event simulation in which patients are transferred between wards of a hospital according to their treatment needs. Entry to each ward is managed by queues, with different policies for queue management and patient prioritisation per ward. Users can manage a simulated hospital, distribute resources between wards and decide how those resources should be prioritised. Simulation results are immediately available for analysis in-browser, including performance against targets, patient flow networks and ward occupancy. The patient flow simulator, freely available at https://khp-informatics.github.io/patient-flow-simulator, is an interactive educational tool that allows healthcare students and professionals to learn important concepts of patient flow and healthcare management.

To date, discrete event stochastic simulations of large scale biological reaction systems are extremely compute-intensive and time-consuming. Besides, it has been widely accepted that spatial factor plays a critical role in the dynamics of most biological reaction systems. The NSM (the Next Sub-Volume Method), a spatial variation of the Gillespie’s stochastic simulation algorithm (SSA), has been proposed for spatially stochastic simulation of those systems. While being able to explore high degree of parallelism in systems, NSM is inherently sequential, which still suffers from the problem of low simulation speed. Fine-grained parallel execution is an elegant way to speed up sequential simulations. Thus, based on the discrete event simulation framework JAMES II, we design and implement a PDES (Parallel Discrete Event Simulation) TW (time warp) simulator to enable the fine-grained parallel execution of spatial stochastic simulations of biological reaction systems using the ANSM (the Abstract NSM), a parallel variation of the NSM. The simulation results of classical Lotka-Volterra biological reaction system show that our time warp simulator obtains remarkable parallel speed-up against sequential execution of the NSM.I.IntroductionThe goal of Systems biology is to obtain system-level investigations of the structure and behavior of biological reaction systems by integrating biology with system theory, mathematics and computer science [1][3], since the isolated knowledge of parts can not explain the dynamics of a whole system. As the complement of “wet-lab” experiments, stochastic simulation, being called the “dry-computational” experiment, plays a more and more important role in computing systems biology [2]. Among many methods explored in systems biology, discrete event stochastic simulation is of greatly importance [4][5][6], since a great number of researches have present that stochasticity or “noise” have a crucial effect on the dynamics of small population biological reaction systems [4][7]. Furthermore, recent research shows that the stochasticity is not only important in biological reaction systems with small population but also in some moderate/large population systems [7].To date, Gillespie’s SSA [8] is widely considered to be the most accurate way to capture the dynamics of biological reaction systems instead of traditional mathematical method [5][9]. However, SSA-based stochastic simulation is confronted with two main challenges: Firstly, this type of simulation is extremely time-consuming, since when the types of species and the number of reactions in the biological system are large, SSA requires a huge amount of steps to sample these reactions; Secondly, the assumption that the systems are spatially homogeneous or well-stirred is hardly met in most real biological systems and spatial factors play a key role in the behaviors of most real biological systems [19][20][21][22][23][24]. The next sub-volume method (NSM) [18], presents us an elegant way to access the special problem via domain partition. To our disappointment, sequential stochastic simulation with the NSM is still very time-consuming, and additionally introduced diffusion among neighbor sub-volumes makes things worse. Whereas, the NSM explores a very high degree of parallelism among sub-volumes, and parallelization has been widely accepted as the most meaningful way to tackle the performance bottleneck of sequential simulations [26][27]. Thus, adapting parallel discrete event simulation (PDES) techniques to discrete event stochastic simulation would be particularly promising. Although there are a few attempts have been conducted [29][30][31], research in this filed is still in its infancy and many issues are in need of further discussion. The next section of the paper presents the background and related work in this domain. In section III, we give the details of design and implementation of model interfaces of LP paradigm and the time warp simulator based on the discrete event simulation framework JAMES II; the benchmark model and experiment results are shown in Section IV; in the last section, we conclude the paper with some future work.II. Background and Related WorkA. Parallel Discrete Event Simulation (PDES)The notion Logical Process (LP) is introduced to PDES as the abstract of the physical process [26], where a system consisting of many physical processes is usually modeled by a set of LP. LP is regarded as the smallest unit that can be executed in PDES and each LP holds a sub-partition of the whole system’s state variables as its private ones. When a LP processes an event, it can only modify the state variables of its own. If one LP needs to modify one of its neighbors’ state variables, it has to schedule an event to the target neighbor. That is to say event message exchanging is the only way that LPs interact with each other. Because of the data dependences or interactions among LPs, synchronization protocols have to be introduced to PDES to guarantee the so-called local causality constraint (LCC) [26]. By now, there are a larger number of synchronization algorithms have been proposed, e.g. the null-message [26], the time warp (TW) [32], breath time warp (BTW) [33] and etc. According to whether can events of LPs be processed optimistically, they are generally divided into two types: conservative algorithms and optimistic algorithms. However, Dematté and Mazza have theoretically pointed out the disadvantages of pure conservative parallel simulation for biochemical reaction systems [31]. B. NSM and ANSM The NSM is a spatial variation of Gillespie’ SSA, which integrates the direct method (DM) [8] with the next reaction method (NRM) [25]. The NSM presents us a pretty good way to tackle the aspect of space in biological systems by partitioning a spatially inhomogeneous system into many much more smaller “homogeneous” ones, which can be simulated by SSA separately. However, the NSM is inherently combined with the sequential semantics, and all sub-volumes share one common data structure for events or messages. Thus, directly parallelization of the NSM may be confronted with the so-called boundary problem and high costs of synchronously accessing the common data structure [29]. In order to obtain higher efficiency of parallel simulation, parallelization of NSM has to firstly free the NSM from the sequential semantics and secondly partition the shared data structure into many “parallel” ones. One of these is the abstract next sub-volume method (ANSM) [30]. In the ANSM, each sub-volume is modeled by a logical process (LP) based on the LP paradigm of PDES, where each LP held its own event queue and state variables (see Fig. 1). In addition, the so-called retraction mechanism was introduced in the ANSM too (see algorithm 1). Besides, based on the ANSM, Wang etc. [30] have experimentally tested the performance of several PDES algorithms in the platform called YH-SUPE [27]. However, their platform is designed for general simulation applications, thus it would sacrifice some performance for being not able to take into account the characteristics of biological reaction systems. Using the similar ideas of the ANSM, Dematté and Mazza have designed and realized an optimistic simulator. However, they processed events in time-stepped manner, which would lose a specific degree of precisions compared with the discrete event manner, and it is very hard to transfer a time-stepped simulation to a discrete event one. In addition, Jeschke etc.[29] have designed and implemented a dynamic time-window simulator to execution the NSM in parallel on the grid computing environment, however, they paid main attention on the analysis of communication costs and determining a better size of the time-window.Fig. 1: the variations from SSA to NSM and from NSM to ANSMC. JAMES II JAMES II is an open source discrete event simulation experiment framework developed by the University of Rostock in Germany. It focuses on high flexibility and scalability [11][13]. Based on the plug-in scheme [12], each function of JAMES II is defined as a specific plug-in type, and all plug-in types and plug-ins are declared in XML-files [13]. Combined with the factory method pattern JAMES II innovatively split up the model and simulator, which makes JAMES II is very flexible to add and reuse both of models and simulators. In addition, JAMES II supports various types of modelling formalisms, e.g. cellular automata, discrete event system specification (DEVS), SpacePi, StochasticPi and etc.[14]. Besides, a well-defined simulator selection mechanism is designed and developed in JAMES II, which can not only automatically choose the proper simulators according to the modeling formalism but also pick out a specific simulator from a serious of simulators supporting the same modeling formalism according to the user settings [15].III. The Model Interface and SimulatorAs we have mentioned in section II (part C), model and simulator are split up into two separate parts. Thus, in this section, we introduce the designation and implementation of model interface of LP paradigm and more importantly the time warp simulator.A. The Mod Interface of LP ParadigmJAMES II provides abstract model interfaces for different modeling formalism, based on which Wang etc. have designed and implemented model interface of LP paradigm[16]. However, this interface is not scalable well for parallel and distributed simulation of larger scale systems. In our implementation, we accommodate the interface to the situation of parallel and distributed situations. Firstly, the neighbor LP’s reference is replaced by its name in LP’s neighbor queue, because it is improper even dangerous that a local LP hold the references of other LPs in remote memory space. In addition, (pseudo-)random number plays a crucial role to obtain valid and meaningful results in stochastic simulations. However, it is still a very challenge work to find a good random number generator (RNG) [34]. Thus, in order to focus on our problems, we introduce one of the uniform RNGs of JAMES II to this model interface, where each LP holds a private RNG so that random number streams of different LPs can be independent stochastically. B. The Time Warp SimulatorBased on the simulator interface provided by JAMES II, we design and implement the time warp simulator, which contains the (master-)simulator, (LP-)simulator. The simulator works strictly as master/worker(s) paradigm for fine-grained parallel and distributed stochastic simulations. Communication costs are crucial to the performance of a fine-grained parallel and distributed simulation. Based on the Java remote method invocation (RMI) mechanism, P2P (peer-to-peer) communication is implemented among all (master-and LP-)simulators, where a simulator holds all the proxies of targeted ones that work on remote workers. One of the advantages of this communication approach is that PDES codes can be transferred to various hardwire environment, such as Clusters, Grids and distributed computing environment, with only a little modification; The other is that RMI mechanism is easy to realized and independent to any other non-Java libraries. Since the straggler event problem, states have to be saved to rollback events that are pre-processed optimistically. Each time being modified, the state is cloned to a queue by Java clone mechanism. Problem of this copy state saving approach is that it would cause loads of memory space. However, the problem can be made up by a condign GVT calculating mechanism. GVT reduction scheme also has a significant impact on the performance of parallel simulators, since it marks the highest time boundary of events that can be committed so that memories of fossils (processed events and states) less than GVT can be reallocated. GVT calculating is a very knotty for the notorious simultaneous reporting problem and transient messages problem. According to our problem, another GVT algorithm, called Twice Notification (TN-GVT) (see algorithm 2), is contributed to this already rich repository instead of implementing one of GVT algorithms in reference [26] and [28].This algorithm looks like the synchronous algorithm described in reference [26] (pp. 114), however, they are essentially different from each other. This algorithm has never stopped the simulators from processing events when GVT reduction, while algorithm in reference [26] blocks all simulators for GVT calculating. As for the transient message problem, it can be neglect in our implementation, because RMI based remote communication approach is synchronized, that means a simulator will not go on its processing until the remote the massage get to its destination. And because of this, the high-costs message acknowledgement, prevalent over many classical asynchronous GVT algorithms, is not needed anymore too, which should be constructive to the whole performance of the time warp simulator.IV. Benchmark Model and Experiment ResultsA. The Lotka-Volterra Predator-prey SystemIn our experiment, the spatial version of Lotka-Volterra predator-prey system is introduced as the benchmark model (see Fig. 2). We choose the system for two considerations: 1) this system is a classical experimental model that has been used in many related researches [8][30][31], so it is credible and the simulation results are comparable; 2) it is simple but helpful enough to test the issues we are interested in. The space of predator-prey System is partitioned into a2D NXNgrid, whereNdenotes the edge size of the grid. Initially the population of the Grass, Preys and Predators are set to 1000 in each single sub-volume (LP). In Fig. 2,r1,r2,r3stand for the reaction constants of the reaction 1, 2 and 3 respectively. We usedGrass,dPreyanddPredatorto stand for the diffusion rate of Grass, Prey and Predator separately. Being similar to reference [8], we also take the assumption that the population of the grass remains stable, and thusdGrassis set to zero.R1:Grass + Prey ->2Prey(1)R2:Predator +Prey -> 2Predator(2)R3:Predator -> NULL(3)r1=0.01; r2=0.01; r3=10(4)dGrass=0.0;dPrey=2.5;dPredato=5.0(5)Fig. 2: predator-prey systemB. Experiment ResultsThe simulation runs have been executed on a Linux Cluster with 40 computing nodes. Each computing node is equipped with two 64bit 2.53 GHz Intel Xeon QuadCore Processors with 24GB RAM, and nodes are interconnected with Gigabit Ethernet connection. The operating system is Kylin Server 3.5, with kernel 2.6.18. Experiments have been conducted on the benchmark model of different size of mode to investigate the execution time and speedup of the time warp simulator. As shown in Fig. 3, the execution time of simulation on single processor with 8 cores is compared. The result shows that it will take more wall clock time to simulate much larger scale systems for the same simulation time. This testifies the fact that larger scale systems will leads to more events in the same time interval. More importantly, the blue line shows that the sequential simulation performance declines very fast when the mode scale becomes large. The bottleneck of sequential simulator is due to the costs of accessing a long event queue to choose the next events. Besides, from the comparison between group 1 and group 2 in this experiment, we could also conclude that high diffusion rate increased the simulation time greatly both in sequential and parallel simulations. This is because LP paradigm has to split diffusion into two processes (diffusion (in) and diffusion (out) event) for two interactive LPs involved in diffusion and high diffusion rate will lead to high proportional of diffusion to reaction. In the second step shown in Fig. 4, the relationship between the speedups from time warp of two different model sizes and the number of work cores involved are demonstrated. The speedup is calculated against the sequential execution of the spatial reaction-diffusion systems model with the same model size and parameters using NSM.Fig. 4 shows the comparison of speedup of time warp on a64X64grid and a100X100grid. In the case of a64X64grid, under the condition that only one node is used, the lowest speedup (a little bigger than 1) is achieved when two cores involved, and the highest speedup (about 6) is achieved when 8 cores involved. The influence of the number of cores used in parallel simulation is investigated. In most cases, large number of cores could bring in considerable improvements in the performance of parallel simulation. Also, compared with the two results in Fig. 4, the simulation of larger model achieves better speedup. Combined with time tests (Fig. 3), we find that sequential simulator’s performance declines sharply when the model scale becomes very large, which makes the time warp simulator get better speed-up correspondingly.Fig. 3: Execution time (wall clock time) of Seq. and time warp with respect to different model sizes (N=32, 64, 100, and 128) and model parameters based on single computing node with 8 cores. Results of the test are grouped by the diffusion rates (Group 1: Sequential 1 and Time Warp 1. dPrey=2.5, dPredator=5.0; Group 2: dPrey=0.25, dPredator=0.5, Sequential 2 and Time Warp 2).Fig. 4: Speedup of time warp with respect to the number of work cores and the model size (N=64 and 100). Work cores are chose from one computing node. Diffusion rates are dPrey=2.5, dPredator=5.0 and dGrass=0.0.V. Conclusion and Future WorkIn this paper, a time warp simulator based on the discrete event simulation framework JAMES II is designed and implemented for fine-grained parallel and distributed discrete event spatial stochastic simulation of biological reaction systems. Several challenges have been overcome, such as state saving, roll back and especially GVT reduction in parallel execution of simulations. The Lotka-Volterra Predator-Prey system is chosen as the benchmark model to test the performance of our time warp simulator and the best experiment results show that it can obtain about 6 times of speed-up against the sequential simulation. The domain this paper concerns with is in the infancy, many interesting issues are worthy of further investigated, e.g. there are many excellent PDES optimistic synchronization algorithms (e.g. the BTW) as well. Next step, we would like to fill some of them into JAMES II. In addition, Gillespie approximation methods (tau-leap[10] etc.) sacrifice some degree of precision for higher simulation speed, but still could not address the aspect of space of biological reaction systems. The combination of spatial element and approximation methods would be very interesting and promising; however, the parallel execution of tau-leap methods should have to overcome many obstacles on the road ahead.AcknowledgmentThis work is supported by the National Natural Science Foundation of China (NSF) Grant (No.60773019) and the Ph.D. Programs Foundation of Ministry of Education of China (No. 200899980004). The authors would like to show their great gratitude to Dr. Jan Himmelspach and Dr. Roland Ewald at the University of Rostock, Germany for their invaluable advice and kindly help with JAMES II.ReferencesH. Kitano, "Computational systems biology." Nature, vol. 420, no. 6912, pp. 206-210, November 2002.H. Kitano, "Systems biology: a brief overview." Science (New York, N.Y.), vol. 295, no. 5560, pp. 1662-1664, March 2002.A. Aderem, "Systems biology: Its practice and challenges," Cell, vol. 121, no. 4, pp. 511-513, May 2005. [Online]. Available: http://dx.doi.org/10.1016/j.cell.2005.04.020.H. de Jong, "Modeling and simulation of genetic regulatory systems: A literature review," Journal of Computational Biology, vol. 9, no. 1, pp. 67-103, January 2002.C. W. Gardiner, Handbook of Stochastic Methods: for Physics, Chemistry and the Natural Sciences (Springer Series in Synergetics), 3rd ed. Springer, April 2004.D. T. Gillespie, "Simulation methods in systems biology," in Formal Methods for Computational Systems Biology, ser. Lecture Notes in Computer Science, M. Bernardo, P. Degano, and G. Zavattaro, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, vol. 5016, ch. 5, pp. 125-167.Y. Tao, Y. Jia, and G. T. Dewey, "Stochastic fluctuations in gene expression far from equilibrium: Omega expansion and linear noise approximation," The Journal of Chemical Physics, vol. 122, no. 12, 2005.D. T. Gillespie, "Exact stochastic simulation of coupled chemical reactions," Journal of Physical Chemistry, vol. 81, no. 25, pp. 2340-2361, December 1977.D. T. Gillespie, "Stochastic simulation of chemical kinetics," Annual Review of Physical Chemistry, vol. 58, no. 1, pp. 35-55, 2007.D. T. Gillespie, "Approximate accelerated stochastic simulation of chemically reacting systems," The Journal of Chemical Physics, vol. 115, no. 4, pp. 1716-1733, 2001.J. Himmelspach, R. Ewald, and A. M. Uhrmacher, "A flexible and scalable experimentation layer," in WSC '08: Proceedings of the 40th Conference on Winter Simulation. Winter Simulation Conference, 2008, pp. 827-835.J. Himmelspach and A. M. Uhrmacher, "Plug'n simulate," in 40th Annual Simulation Symposium (ANSS'07). Washington, DC, USA: IEEE, March 2007, pp. 137-143.R. Ewald, J. Himmelspach, M. Jeschke, S. Leye, and A. M. Uhrmacher, "Flexible experimentation in the modeling and simulation framework james ii-implications for computational systems biology," Brief Bioinform, vol. 11, no. 3, pp. bbp067-300, January 2010.A. Uhrmacher, J. Himmelspach, M. Jeschke, M. John, S. Leye, C. Maus, M. Röhl, and R. Ewald, "One modelling formalism & simulator is not enough! a perspective for computational biology based on james ii," in Formal Methods in Systems Biology, ser. Lecture Notes in Computer Science, J. Fisher, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, vol. 5054, ch. 9, pp. 123-138. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-68413-8_9.R. Ewald, J. Himmelspach, and A. M. Uhrmacher, "An algorithm selection approach for simulation systems," pads, vol. 0, pp. 91-98, 2008.Bing Wang, Jan Himmelspach, Roland Ewald, Yiping Yao, and Adelinde M Uhrmacher. Experimental analysis of logical process simulation algorithms in james ii[C]// In M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, editors, Proceedings of the Winter Simulation Conference, IEEE Computer Science, 2009. 1167-1179.Ewald, J. Rössel, J. Himmelspach, and A. M. Uhrmacher, "A plug-in-based architecture for random number generation in simulation systems," in WSC '08: Proceedings of the 40th Conference on Winter Simulation. Winter Simulation Conference, 2008, pp. 836-844.J. Elf and M. Ehrenberg, "Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases." Systems biology, vol. 1, no. 2, pp. 230-236, December 2004.K. Takahashi, S. Arjunan, and M. Tomita, "Space in systems biology of signaling pathways? Towards intracellular molecular crowding in silico," FEBS Letters, vol. 579, no. 8, pp. 1783-1788, March 2005.J. V. Rodriguez, J. A. Kaandorp, M. Dobrzynski, and J. G. Blom, "Spatial stochastic modelling of the phosphoenolpyruvate-dependent phosphotransferase (pts) pathway in escherichia coli," Bioinformatics, vol. 22, no. 15, pp. 1895-1901, August 2006.D. Ridgway, G. Broderick, and M. Ellison, "Accommodating space, time and randomness in network simulation," Current Opinion in Biotechnology, vol. 17, no. 5, pp. 493-498, October 2006.J. V. Rodriguez, J. A. Kaandorp, M. Dobrzynski, and J. G. Blom, "Spatial stochastic modelling of the phosphoenolpyruvate-dependent phosphotransferase (pts) pathway in escherichia coli," Bioinformatics, vol. 22, no. 15, pp. 1895-1901, August 2006.W. G. Wilson, A. M. Deroos, and E. Mccauley, "Spatial instabilities within the diffusive lotka-volterra system: Individual-based simulation results," Theoretical Population Biology, vol. 43, no. 1, pp. 91-127, February 1993.K. Kruse and J. Elf. Kinetics in spatially extended systems. In Z. Szallasi, J. Stelling, and V. Periwal, editors, System Modeling in Cellular Biology. From Concepts to Nuts and Bolts, pages 177–198. MIT Press, Cambridge, MA, 2006.M. A. Gibson and J. Bruck, "Efficient exact stochastic simulation of chemical systems with many species and many channels," The Journal of Physical Chemistry A, vol. 104, no. 9, pp. 1876-1889, March 2000.R. M. Fujimoto, Parallel and Distributed Simulation Systems (Wiley Series on Parallel and Distributed Computing). Wiley-Interscience, January 2000.Y. Yao and Y. Zhang, “Solution for analytic simulation based on parallel processing,” Journal of System Simulation, vol. 20, No.24, pp. 6617–6621, 2008.G. Chen and B. K. Szymanski, "Dsim: scaling time warp to 1,033 processors," in WSC '05: Proceedings of the 37th conference on Winter simulation. Winter Simulation Conference, 2005, pp. 346-355.M. Jeschke, A. Park, R. Ewald, R. Fujimoto, and A. M. Uhrmacher, "Parallel and distributed spatial simulation of chemical reactions," in 2008 22nd Workshop on Principles of Advanced and Distributed Simulation. Washington, DC, USA: IEEE, June 2008, pp. 51-59.B. Wang, Y. Yao, Y. Zhao, B. Hou, and S. Peng, "Experimental analysis of optimistic synchronization algorithms for parallel simulation of reaction-diffusion systems," High Performance Computational Systems Biology, International Workshop on, vol. 0, pp. 91-100, October 2009.L. Dematté and T. Mazza, "On parallel stochastic simulation of diffusive systems," in Computational Methods in Systems Biology, M. Heiner and A. M. Uhrmacher, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, vol. 5307, ch. 16, pp. 191-210.D. R. Jefferson, "Virtual time," ACM Trans. Program. Lang. Syst., vol. 7, no. 3, pp. 404-425, July 1985.J. S. Steinman, "Breathing time warp," SIGSIM Simul. Dig., vol. 23, no. 1, pp. 109-118, July 1993. [Online]. Available: http://dx.doi.org/10.1145/174134.158473 S. K. Park and K. W. Miller, "Random number generators: good ones are hard to find," Commun. ACM, vol. 31, no. 10, pp. 1192-1201, October 1988.

It is a challenging work to simulate wind and waves in virtual scenes of marine simulators. In this paper, a divergence-free position based fluid (DFPBF) framework is introduced for ocean wave modeling in marine simulators. We introduce a set of constant density constraints and divergence-free velocity constraints to enforce incompressibility. By adjusting the position distribution of fluid particles, the particle density is forced to be constant. Constraining the divergence-free velocity field can keep the density change rate at zero. When correcting the position and velocity of particles, we introduced a relaxation correction scheme to accelerate the convergence of the framework. The simulation results show that as the scene scale expands and the number of fluid particles increases, this acceleration effect will be more significant. Secondly, we propose a novel particle-based three-dimensional stochastic fluctuating wind field. The Perlin noise is introduced to disturb the constant horizontal wind field to form a stochastic wind field. On this basis, a stochastic fluctuating wind field simulation framework is proposed. By adjusting the pulse period and pulse width, users can flexibly control the fluid turnover under the action of the wind field. This wind field framework can be easily integrated into the DFPBF model. Based on this wind field model, we simulated some typical wind wave scenarios, including interaction scenarios with lighthouse and lifebuoy, and verified the effectiveness of the wind field model.

Abstract. This paper presents a physical (PHY) layer simulator of the IEEE 802.11p standard for Wireless Access in Vehicular Environments (WAVE). This simulator allows the emulation of data transmission via different radio channels as well as the analysis of the resulting system behavior. The PHY layer simulator is part of an integrated simulation platform including a traffic model to generate realistic mobility of vehicles and a 3D ray-optical model to calculate the multipath propagation channel between transmitter and receiver. Besides deterministic channel modeling by means of ray-optical modeling, the simulator can also be used with stochastic channel models of typical vehicular scenarios. With the aid of this PHY layer simulator and the integrated channel models, the resulting performance of the system in terms of bit and packet error rates of different receiver designs can be analyzed in order to achieve a robust data transmission.

The importance of risk assessment in evaluating existing and new farm operations and new projects is emphasized in this paper. The Top Management Farm Business Simulator is a whole-farm financial-planning model that simulates up to 250 observations of farm income, expenses, cash flows and net worth for periods of up to 15 yr, using stochastic product prices and yields. Individual probability functions, as well as cross-correlations among stochastic variables, can be specified. Key words: Risk assessment, financial planning, crop management

We have developed a new solar irradiation model and implemented it in the SunIrradiance photovoltaic cell/module simulator. This model uses stochastic methods to generate the hourly distribution of solar irradiation on a horizontal or inclined surface from monthly irradiation values on the horizontal surface of a selected location and was verified with the measured irradiance data in Ljubljana, located in Central Europe. The new model shows better simulation results with regard to the share of the diffuse irradiation in the region than the other models. The simulation results show that the new solar irradiation model is excellent for photovoltaic system simulations of single junction PV technologies.

Abstract Human resources are still utilized in many manufacturing systems, so the development of these processes should also focus on the performance of the operators. The optimization of production systems requires accurate and reliable models. Due to the complexity and uncertainty of the human behavior, the modeling of the operators is a challenging task. Our goal is to develop a worker movement diagram based model that considers the stochastic nature of paced open conveyors. The problem is challenging as the simulator has to handle the open nature of the workstations, which means that the operators can work ahead or try to work off their backlog, and due to the increased flexibility of the moving patterns the possible crossings which could lead to the stopping of the conveyor should also be modeled. The risk of such micro-stoppings is calculated by Monte-Carlo simulation. The applicability of the simulator is demonstrated by a well-documented benchmark problem of a wire-harness production process.

This work introduces MACACO, a macroscopic calcium currents simulator. It provides a parameter-sweep framework which computes macroscopic Ca 2+ currents from the individual aggregation of unitary currents, using a stochastic model for L-type Ca 2+ channels. MACACO uses a simplified 3-state Markov model to simulate the response of each Ca 2+ channel to different voltage inputs to the cell. In order to provide an accurate systematic view for the stochastic nature of the calcium channels, MACACO is composed of an experiment generator, a central simulation engine and a post-processing script component. Due to the computational complexity of the problem and the dimensions of the parameter space, the MACACO simulation engine employs a grid-enabled task farm. Having been designed as a computational biology tool, MACACO heavily borrows from the way cell physiologists conduct and report their experimental work.

PROPAGATOR is a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The model uses high-resolution information such as topography and vegetation cover considering different types of vegetation. Input parameters are wind speed and direction and the ignition point. Dead fine fuel moisture content and firebreaks—fire fighting strategies can also be considered. The fire spread probability depends on vegetation type, slope, wind direction and speed, and fuel moisture content. The fire-propagation speed is determined through the adoption of a Rate of Spread model. PROPAGATOR simulates independent realizations of one stochastic fire propagation process, and at each time-step gives as output a map representing the probability of each cell of the domain to be affected by the fire. These probabilities are obtained computing the relative frequency of ignition of each cell. The model capabilities are assessed by reproducing a set of past Mediterranean fires occurred in different countries (Italy and Spain), using when available the real fire fighting patterns. PROPAGATOR simulated such scenarios with affordable computational resources and with short CPU-times. The outputs show a good agreement with the real burned areas, demonstrating that the PROPAGATOR can be useful for supporting decisions in Civil Protection and fire management activities.

In order to evaluate a communication system, we need to model the propagation channel of the relevant environments pertaining to that communication. In this paper, we propose a Geometry-Based Stochastic Channel Modeling approach to build up propagation channel simulations to assess the performance of vehicle-to-vehicle wireless communications. Our methodology allows the simulation of dynamic scenarios, with an electromagnetic simulator, to emulate typical propagation environments (rural, highway and urban-like propagation channels). Simple metallic plates are used to represent scatterers in the simulated geometric configurations. The common characteristics defining a propagation channel such as delay spread, angle of arrival distribution, and the delay-Doppler spectrum are obtained through adjustment of the number and location of those simple metallic plates.

Abstract A fire behavior module was developed for the California Fire Economics Simulator version 2 (CFES2), a stochastic simulation model of initial attack on wildland fire used by the California Department of Forestry and Fire Protection. Fire rate of spread (ROS) and fire dispatch level (FDL) for simulated fires "occurring" on the same day are determined by making coordinated draws from compound distributions characterizing 2 PM fire behavior indices such as ROS, then adjusting these draws using diurnal adjustment coefficients derived from hourly fire weather observations. Statistical examination of historical fire occurrence and predicted behavior data validated CFES2's use of independent fire occurrence and fire behavior modeling processes. For. Sci. 45(4):492-499.

Abstract An in-depth analysis of resistive switching (RS) in unipolar devices is performed by means of a new simulator based on resistive circuit breakers of different features. The forming, set and reset processes are described in terms of the stochastic formation and rupture of conductive filaments (CFs) of several branches in the dielectric. Both, the electric field and temperature dependencies are incorporated in the simulation. The simulation tool was tuned with experimental data of devices fabricated making use of the Ni/HfO2/Si stack. The variability and the stochastic behavior are characterized and reproduced correctly by simulation to understand the physics behind RS. Reset curves with several current steps are explained considering the rupture of different branches of the CF. The simulation approach allows to connect in a natural manner to compact modeling solutions for the devices under study.

This paper is devoted to an exhaustive presentation of a fast computation numerical tool, dedicated to the simulation of transient currents induced by stochastic events in microelectronic devices. This is a part of a numerical platform, SITARe, combining a spice simulator with the semi-analytical model presented here. The paper describes the theoretical model, the calibration. An instance of application illustrates the ability of the tool.

The process of validating a stochastic simulation model involves the comparison of data generated by the model with corresponding data from the real system. Instead of applying statistical tests to determine whether the model adequately represents the real system, an alternate approach is to estimate the error that will result when the model is used to draw inferences about the real system. Regression methodology is proposed for estimating this error as a function of the levels of the input variables of the model. Confidence intervals for expected error and prediction intervals for actual error are given. An example of estimating the error in the volume predictions of a stochastic forest stand simulator is given.

The Stochastic Nanoparticle Simulator (SNAPS) has been developed to investigate Polycyclic Aromatic Hydrocarbon (PAH) growth in combustion. Simulations elucidated novel, atomistic insight into the chemical composition and morphology of nascent PAHs.

Together with significant water savings that onsite greywater reuse (GWR) may provide, it may also affect the performance of urban sewer systems and wastewater treatment plants (WWTPs). In order to examine these effects, an integrated stochastic simulation system for GWR in urban areas was developed. The model includes stochastic generators of domestic wastewater streams and gross solids (GSs), a sewer network model which includes hydrodynamic simulation and a GS transport module, and a dynamic process model of the WWTP. The developed model was applied to a case study site in Israel. For the validation of the sewer simulator, field experiments in a real sewer segment were conducted. The paper presents the integration and implementation of these modules and depicts the results of the effects of various GWR scenarios on GS movement in sewers and on the performance of the WWTP.

AbstractThis paper proposes a novel computationally efficient stochastic spectral projection based approach to Bayesian inversion of a computer simulator with high dimensional parametric and model structure uncertainty. The proposed method is based on the decomposition of the solution into its mean and a random field using a generic Karhunen-Loève expansion. The random field is represented as a convolution of separable Hilbert spaces in stochastic and spatial dimensions that are spectrally represented using respective orthogonal bases. In particular, the present paper investigates generalized polynomial chaos bases for the stochastic dimension and eigenfunction bases for the spatial dimension. Dynamic orthogonality is used to derive closed-form equations for the time evolution of mean, spatial and the stochastic fields. The resultant system of equations consists of a partial differential equation (PDE) that defines the dynamic evolution of the mean, a set of PDEs to define the time evolution of eigenfunction bases, while a set of ordinary differential equations (ODEs) define dynamics of the stochastic field. This system of dynamic evolution equations efficiently propagates the prior parametric uncertainty to the system response. The resulting bi-orthogonal expansion of the system response is used to reformulate the Bayesian inference for efficient exploration of the posterior distribution. The efficacy of the proposed method is investigated for calibration of a 2D transient diffusion simulator with an uncertain source location and diffusivity. The computational efficiency of the method is demonstrated against a Monte Carlo method and a generalized polynomial chaos approach.

Abstract Motivation Stochastic gene expression simulations often assume steady-state transcript levels, or they model transcription in more detail than translation. Moreover, they lack accessible programing interfaces, which limit their utility. Results We present Pinetree, a step-wise gene expression simulator with codon-specific translation rates. Pinetree models both transcription and translation in a stochastic framework with individual polymerase and ribosome-level detail. Written in C++ with a Python front-end, any user familiar with Python can specify a genome and simulate gene expression. Pinetree was designed to be efficient and scale to simulate large plasmids or viral genomes. Availability and implementation Pinetree is available on GitHub (https://github.com/benjaminjack/pinetree) and the Python Package Index (https://pypi.org/project/pinetree/).