1. Model Complexity Transformation

It is investigated how to systematically derive models of different complexity. These may be simplified models in the same formalism [50] but also more abstract models in a different representation [44]. This methodology can be applied, e.g.,

• to perform optimization with increasingly complex models, which may be more likely to find a global optimum [20],
• to allow vendor models (destined to replace electronic data sheets) to be combined into one extensive and complex base model that can be used in a reduced form for the different design and operation tasks (e.g., control design, performance assessment, and model-based diagnosis) [38],
• to design intelligent numerical solvers that adapt the complexity of the model to the efficiency requirements (e.g., real-time simulation constraints) [30],
• to support reactive learning environments (so called microworlds) by increasingly adding detail to the world model [42], and
• to infer the required level of detail of model parts in different representations to ensure consistency of analysis results of the overall combined model against given criteria.

Note that in general it may be possible to automatically add model detail as well as to automatically reduce complexity of a base model [9].