One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability helps people better understand and analyze the rules

The combination of process modeling and simulation-based analysis provides a quantitative approach to analyze business processes, and to evaluate design alternatives before committing the required resources, to properly align operations with business strategies, improve operational efficiency, and gain competitive advantage. However, the use of simulation-based analysis is still limited in practice

Research in computer architecture is commonly done using software simulators. The simulation speed of such simulators is therefore critical to the rate of progress in research. One of the less commonly used ways to increase the simulation speed is to decompose the benchmark’s execution into contiguous chunks of instructions and simulate these chunks in parallel. Two issues arise from this approach

We develop a theory of superdense time that encompasses existing uses of superdense time in discrete event simulations and points to new forms that have not previously been explored. A central feature of our development is a set of axioms for superdense time. The sufficiency of these axioms is demonstrated by using them to prove that a general model of a discrete event simulation procedure, expressed

A truncated Lévy subordinator is a Lévy subordinator in R+ with Lévy measure restricted from above by a certain level b. In this article, we study the path and distribution properties of this type of process in detail and set up an exact simulation framework based on a marked renewal process. In particular, we focus on a typical specification of truncated Lévy subordinator, namely the truncated stable

Time-inhomogeneous queueing models play an important role in service systems modeling. Although the transient solutions of corresponding continuous-time Markov chains (CTMCs) are more precise than methods using stationary approximations, most authors consider their computational costs prohibitive for practical application. This article presents a new variant of the uniformization algorithm that utilizes

This paper provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise we focus on manufacturing simulations, but the underlying methodology in mostly independent from this application domain. In specific, we propose and discuss a methodology for designing, executing, and analyzing large scale simulation experiments with a broad coverage of possible

Simulations are often used for the design of complex systems as they allow to explore the design space without the need to build several prototypes. Over the years, the simulation accuracy, as well as the associated computational cost has increased significantly, limiting the overall number of simulations during the design process. Therefore, metamodeling aims to approximate the simulation response

Cells exhibit stochastic behavior when the number of molecules is small. Hence a stochastic reaction-diffusion simulator capable of working at scale can provide a more accurate view of molecular dynamics within the cell. This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. To the best

We describe the design and prototype implementation of Indemics (Interactive EpidemicSimulation)-a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and

Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this paper we

The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication,