Model management for hypothesis-driven simulation experiment workflows

Çam, Sema
With today's breakthroughs in computational science and engineering, research experts can now simulate a lot of experiments on computers. Experiment specification is aided by frameworks and support systems for reusability and reproducibility of scientific research, as well as domain-specific languages, domain models, ontologies, data models, statistical analysis methods, and other types of tools and assets with related formalisms. Despite this, most frameworks or support tools for experiment specification ignore hypotheses and lack a procedure based on properly stated hypotheses. The main issue with a lack of hypotheses in the experimental process is that an experiment's credibility and repeatability can be harmed by an erroneous or inadequate record. Furthermore, the diversity of models, metamodels, tools, and data for testing bring the need for Global Model Management (GMM). In that sense, GMM leverages documenting, sharing, reusability, and replicability of simulation experiments by employing Model-Driven Engineering methodologies. This thesis demonstrates how to use GMM to facilitate simulation experimentation with explicit hypotheses as a scientific workflow and proposes an extension to the Simulation Experiment Description Markup Language (SED-ML) that involves explicit specification of the hypothesis targeted in the simulation experiment. A megamodel, or registry for models and metamodels, is created particularly to serve as a repository for managing the artifacts used in a simulation project. All steps of a simulation experiment, including specification, input data production, experiment execution, and output data analysis, are effectively addressed by the megamodel. Then, using case studies, the applicability of GMM to simulation experiments is demonstrated. GMM, in our view, provides a solid framework for managing both experiment assets and experiment processes.


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An simulation has become a major tool in studying discrete manufacturing systems, experimental design issues have started to receive much attention. Although the importance of proper experimentation is often emphasized in the literature on discrete event simulation, it is neglected in most practical simulation studies. The main reason for this neglect is that the design and analysis of a simulation experiment require expertise in experimental design methodology as well as familiarity with traditional statis...
Supporting Simulation Experiments with Megamodeling
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Recent developments in computational science and engineering allow a great deal of experimental work to be conducted through computer simulation. In a simulation experiment, a model of the phenomena to be studied is run in a computing environment under varying model and environment settings. As models are adjusted to experimental procedures and execution environments, variations arise. Models also evolve in time. Thus, models must be managed. We propose to bring Global Model Management (GMM) to bear on simu...
Model based preliminary design and optimization of aircrafts
Özdemir, Mustafa; Kurtuluş, Dilek Funda; Department of Aerospace Engineering (2022-8)
This study aims to create an interdisciplinary, multi-level design approach that is based on six degrees of freedom mathematical model. The model provides all disciplines to communicate with each other simultaneously, thanks to an automated process chain system. The main advantage of automatization of analyses for the design process is observing interdisciplinary effects clearly without wasting time and money. Instead of using only empirical relations, in this study, calculations are originating from trim a...
Model-driven engineering framework for replicable simulation experiments
Dayıbaş, Orçu; Oğuztüzün, Halil.; Department of Computer Engineering (2019)
Simulation experiments allow the user to capture the specific variability of multiple interdependent processes. The use of simulation models is widely accepted by practitioners from diverse areas of applied sciences. Therefore, simulation experiments are an essential part of computational science and engineering. Nevertheless, simulations are rarely replicated due to reuse and maintenance challenges related to models and data. In this respect, it is proposed that some crucial and labor intensive parts of th...
Hypothesis-driven simulation experiments with an extension to SED-ML
Cam, S.; Oğuztüzün, Mehmet Halit S.; Yilmaz, L. (2022-03-01)
Most of the frameworks or assistance systems for experiment specification do not provide a process explicitly based on formally specified hypotheses. This deficiency leads to inaccurate or insufficient record of an experiment, decreasing the trustworthiness and reproducibility of the experiment. Moreover, the wide variety of models, metamodels, tools, and data for experimentation requires Global Model Management (GMM) that is utilizing Model-Driven Engineering techniques, facilitates documentation, sharing,...
Citation Formats
S. Çam, “Model management for hypothesis-driven simulation experiment workflows,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.