scholarly journals Model laboratories: A quick-start guide for design of simulation experiments for dynamic systems models

2020 ◽  
Vol 434 ◽  
pp. 109246
Author(s):  
Benjamin L. Turner
2016 ◽  
Author(s):  
Galina Ivanovna Popova ◽  
◽  
Yuliya Nikolaevna Prilepskaya ◽  

2017 ◽  
Vol 24 (s1) ◽  
pp. 32-37 ◽  
Author(s):  
Marian Cichy ◽  
Zbigniew Kneba ◽  
Jacek Kropiwnicki

AbstractWith a single approach to modeling elements of different physical nature, the method of Bond Graph (BG) is particularly well suited for modeling energy systems consisting of mechanical, thermal, electrical and hydraulic elements that operate in the power system engine room. The paper refers to the earlier presented [2] new concept of thermal process modeling using the BG method. The authors own suggestions for determining causality in models of thermal processes created by the said concept were given. The analysis of causality makes it possible to demonstrate the model conflicts that prevent the placement of state equations which allows for the direct conduct of simulation experiments. Attention has been drawn to the link between the energy systems models of thermal processes with models of elements of different physical nature. Two examples of determining causality in models of complex energy systems of thermal elements have been presented. The firs relates to the electrical system associated with the process of heat exchange. The second is a model of the mechanical system associated with the thermodynamic process.


2016 ◽  
Author(s):  
Josif A. Boguslavskiy

2002 ◽  
Vol 6 (4) ◽  
pp. 304-315 ◽  
Author(s):  
Charles S. Carver ◽  
Michael F. Scheier

This article addresses the convergence and complementarity between self-regulatory control-process models of behavior and dynamic systems models. The control-process view holds that people have a goal in mind and try to move toward it (or away from it), monitoring the extent to which a discrepancy remains between the goal and one's present state and taking steps to reduce the discrepancy (or enlarge it). Dynamic systems models tend to emphasize a bottom-up self-organization process, in which a coherence arises from among many simultaneous influences, moving the system toward attractors and away from repellers. We suggest that these differences in emphasis reflect two facets of a more complex reality involving both types of processes. Discussion focuses on how self-organization may occur within constituent elements of a feedback system—the input function, the output function, and goal values being used by the system—and how feedback processes themselves can reflect self-organizing tendencies.


2003 ◽  
Vol 15 (3) ◽  
pp. 641-669 ◽  
Author(s):  
ISABELA GRANIC ◽  
TOM HOLLENSTEIN

A survey of dynamic systems (DS) methods appropriate for testing systems-based models in developmental psychopathology is provided. The rationale for developing new methods for the field is reviewed first. In line with other scholars, we highlight the fundamental incompatibility between developmentalists' organismic, open systems models and the mechanistic research methods with which these models are tested. Key DS principles are explained and their commensurability with developmental psychopathologists' core theoretical concerns are discussed. Next, a survey of research designs and methodological techniques currently being used and refined by developmental DS researchers is provided. The strengths and limitations of each approach are discussed throughout this review. Finally, we elaborate on one specific dynamic systems method, state space grids, which addresses many of the limitations of previous DS techniques and may prove most useful for the discipline. This approach was developed as a middle road between DS methods that are mathematically heavy on the one hand and purely descriptive on the other. Examples of developmental and clinical studies that have applied state space grids are reviewed and suggestions for future analyses are made. We conclude with some implications for the application of this new methodology for studying change processes in clinical research.


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