Abstract
Complex systems’ modeling and simulation are powerful ways to investigate a multitude of natural phenomena providing extended knowledge on their structure and behavior. However, enhanced modeling and simulation require integration of various data and knowledge sources, models of various kinds (data-driven models, numerical models, simulation models, etc.), and intelligent components in one composite solution. Growing complexity of such composite model leads to the need of specific approaches for management of such model. This need extends where the model itself becomes a complex system. One of the important aspects of complex model management is dealing with the uncertainty of various kinds (context, parametric, structural, and input/output) to control the model. In the situation where a system being modeled, or modeling requirements change over time, specific methods and tools are needed to make modeling and application procedures (metamodeling operations) in an automatic manner. To support automatic building and management of complex models we propose a general evolutionary computation approach which enables managing of complexity and uncertainty of various kinds. The approach is based on an evolutionary investigation of model phase space to identify the best model’s structure and parameters. Examples of different areas (healthcare, hydrometeorology, and social network analysis) were elaborated with the proposed approach and solutions.
Highlights
Today the area of modeling and simulation of complex systems evolves rapidly
We consider a combination of Evolutionary computation (EC) and data-driven approaches as a tool for building intelligent solutions for more precise and systematic managing uncertainty and providing the required level of automation, adaptability, and extendibility
Current research extends the concept beyond ensemble-based simulation. It is mainly focused on complex modeling in general with identification of key model management procedures and important artifacts which can be used for model development and application
Summary
Today the area of modeling and simulation of complex systems evolves rapidly. A complex system [1] is usually characterized by a large number of elements, complex long-distance interaction between elements, and multiscale variety. In many cases, the complexity of a model does not mimic the complexity of a system under investigation (at least exactly). It leads to additional issues in managing a complex model during identification, calibration, data assimilation, verification, validation, and application. We are trying to develop a unified conceptual and technological approach to support core operation with a complex model by distinguishing concepts and operations on model, data, and system levels. We consider a combination of EC and data-driven approaches as a tool for building intelligent solutions for more precise and systematic managing (and lowering) uncertainty and providing the required level of automation, adaptability, and extendibility
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
