Model-driven engineering framework for replicable simulation experiments

Dayıbaş, Orçu
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 the simulation experiments could be replaced or supported by model transformations. This work focuses on model-driven engineering practices to enable replicable and reusable simulation experiments. These practices are used to devote researchers’ time to analyze the system under investigation rather than dealing with low level details to create a working environment. The results of our framework development work and our research directions are presented.


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,...
Dynamic simulation metamodeling using MARS: A case of radar simulation
Bozagac, Doruk; Batmaz, İnci; Oğuztüzün, Mehmet Halit S. (2016-06-01)
Dynamic system simulations require relating the inputs to the multivariate output which can be a function of time space coordinates. In this work, we propose a methodology for the metamodeling of dynamic simulation models via Multivariate Adaptive Regression Splines (MARS). To handle incomplete output processes, where the simulation model does not produce an output in some steps due to missing inputs, we have devised a two-stage metamodeling scheme. The methodology is demonstrated on a dynamic radar simulat...
Supporting Simulation Experiments with Megamodeling
Cam, Sema; Dayibas, Orcun; Gorur, Bilge K.; Oğuztüzün, Mehmet Halit S.; Yilmaz, Levent; Chakladar, Sritika; Doud, Kyle; Smith, Alice E.; Teran-Somohano, Alejandro (SCITEPRESS, AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL; 2018-01-01)
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 management for hypothesis-driven simulation experiment workflows
Çam, Sema; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2022-9-05)
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 i...
Modeling of various biological networks via LCMARS
AYYILDIZ DEMİRCİ, EZGİ; Purutçuoğlu Gazi, Vilda (Elsevier BV, 2018-09-01)
In system biology, the interactions between components such as genes, proteins, can be represented by a network. To understand the molecular mechanism of complex biological systems, construction of their networks plays a crucial role. However, estimation of these biological networks is a challenging problem because of their high dimensional and sparse structures. Several statistical methods are proposed to overcome this issue. The Conic Multivariate Adaptive Regression Splines (CMARS) is one of the recent n...
Citation Formats
O. Dayıbaş, “Model-driven engineering framework for replicable simulation experiments,” Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.