Model Continuity in Discrete Event Simulation: A Framework for Model-Driven Development of Simulation Models.

Most of the well known modeling and simulation methodologies state the importance of conceptual modeling in simulation studies and they suggest the use of conceptual models during the simulation model development process. However, only a limited number of methodologies refers to howto move from a conceptual model to an executable simulation model. Besides, existing modeling and simulation methodologies do not typically provide a formal method for model transformations between the models in different stages of the development process. Hence, in the current M&S practice, model continuity is usually not fulfilled. In this article, a model driven development framework for modeling and simulation is in order to bridge the gap between different stages of a simulation study and to obtain model continuity. The applicability of the framework is illustrated with a prototype modeling environment and a case study in the discrete event simulation domain

A tutorial on simulation conceptual modeling

© 2017 IEEE. Conceptual modeling is the abstraction of a simulation model from the part of the real world it is representing; in other words, choosing what to model, and what not to model. This is generally agreed to be the most difficult, least understood, but probably the most important activity to be carried out in a simulation study. In this tutorial we explore the definition, requirements and approach to conceptual modeling. First we ask 'where is the model?' We go on to define the term 'conceptual model', to identify the artefacts of conceptual modeling, and to discuss the purpose and benefits of a conceptual model. In so doing we identify the role of conceptual modeling in the simulation project life-cycle. The discussion then focuses on the requirements of a conceptual model, the approaches for documenting a conceptual model, and frameworks for guiding the conceptual modeling activity. One specific framework is described and illustrated in more detail. The tutorial concludes with a discussion on the level of abstraction

Towards NLP-Based Conceptual Modeling Frameworks

This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common process and generating similar “blueprints” facilitating communication and collaboration efforts. Overall, NLP presents an opportunity for the M&S community to engage with stakeholders and scholars across domains in the simulation development process, lowering entry barriers and increasing participation

Enhancing Simulation Composability and Interoperability Using Conceptual/Semantic/Ontological Models

(First paragraph) Two emerging trends in Modeling and Simulation (M&S) are beginning to dovetail in a potentially highly productive manner, namely conceptual modeling and semantic modeling. Conceptual modeling has existed for several decades, but its importance has risen to the forefront in the last decade (Taylor and Robinson, 2006; Robinson, 2007). Also, during the last decade, progress on the Semantic Web has begun to influence M&S, with the development of general modeling ontologies (Miller et al, 2004), as well as ontologies for modeling particular domains (Durak, 2006). An ontology, which is a formal specification of a conceptualization (Gruber et al, 1993), can be used to rigorously define a domain of discourse in terms of classes/concepts, properties/relationships and instances/individuals. For the Semantic Web, ontologies are typically specified using the Web Ontology Language (OWL). Although, conceptual modeling is broader than just semantics (it includes additional issues such as pragmatics (Tolk et al, 2008)), progress in the Semantic Web and ontologies is certainly beneficial to conceptual modeling. Benefits are accrued in many ways including the large knowledge bases being placed on the Web in numerous fields in which simulation studies are conducted and the powerful reasoning algorithms based on description logic being developed that allow the consistency of large specifications to be checked

Elements of a Theory of Simulation

Unlike computation or the numerical analysis of differential equations, simulation does not have a well established conceptual and mathematical foundation. Simulation is an arguable unique union of modeling and computation. However, simulation also qualifies as a separate species of system representation with its own motivations, characteristics, and implications. This work outlines how simulation can be rooted in mathematics and shows which properties some of the elements of such a mathematical framework has. The properties of simulation are described and analyzed in terms of properties of dynamical systems. It is shown how and why a simulation produces emergent behavior and why the analysis of the dynamics of the system being simulated always is an analysis of emergent phenomena. A notion of a universal simulator and the definition of simulatability is proposed. This allows a description of conditions under which simulations can distribute update functions over system components, thereby determining simulatability. The connection between the notion of simulatability and the notion of computability is defined and the concepts are distinguished. The basis of practical detection methods for determining effectively non-simulatable systems in practice is presented. The conceptual framework is illustrated through examples from molecular self-assembly end engineering.Comment: Also available via http://studguppy.tsasa.lanl.gov/research_team/ Keywords: simulatability, computability, dynamics, emergence, system representation, universal simulato

Optimization and evaluation of variability in the programming window of a flash cell with molecular metal-oxide storage

We report a modeling study of a conceptual nonvolatile memory cell based on inorganic molecular metal-oxide clusters as a storage media embedded in the gate dielectric of a MOSFET. For the purpose of this paper, we developed a multiscale simulation framework that enables the evaluation of variability in the programming window of a flash cell with sub-20-nm gate length. Furthermore, we studied the threshold voltage variability due to random dopant fluctuations and fluctuations in the distribution of the molecular clusters in the cell. The simulation framework and the general conclusions of our work are transferrable to flash cells based on alternative molecules used for a storage media

Chapter 22 Simulation Modeling as a Policy Tool

This chapter describes, justifies, presents the pros and cons of and illustrates the use of simulation modeling as a handy, cost-effective and agile tool for policymakers. Simulation modeling is flexible enough to accommodate different levels of detail, precision and time frameworks. It also serves the purpose of a concrete communication platform that facilitates scenario analysis, what-if alternatives and forward looking. We specifically define agent-based modeling within the larger simulation domain, provide a brief overview of other computation modeling methodologies and discuss the concepts of multiple models, verification, validation and calibration. The conceptual framework section closes with a discussion of advantages and disadvantages of using simulation modeling for policy at various stages of implementation. Finally, we present a panorama of actual applications of simulation modeling in policy, with an emphasis on economic analysis