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optiSLang automatically identifies the relevant input and output parameters and quantifies the forecast quality with the help of the Coefficient of Prognosis ( CoP) and the Metamodel of Optimal Prognosis (MOP). A predictable prognosis quality is the key to an efficient optimization. Thus, a "no run too much" philosophy can be implemented to minimize solver calls.
As a consequence, even optimization tasks involving a large number of variables, scattering parameter as well as non-linear system behavior can be solved. optiSLang’s Best-Practice-Management automatically selects the appropriate algorithms, such as gradient methods, genetic algorithms, evolutionary strategies or Adaptive Response Surface Methods. Furthermore, all methods of optimization and stochastic analysis can be combined in regard to the particular task. optiSLang makes you ready to meet the full range of parametric studies to innovate and accelerate your virtual product development.
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