Abstract Scope |
Global optimization based on AI or active learning. That is, the user of our software does not have to select a suitable design since the software generates its own agile design based on the answer. Because of this, our software does not scale the number of tests with the granularity of the search field.
The number of experiments required does not grow exponentially with the number of parameters/dimensions. That implies we require less genuine trials in general than any other DoE, especially when you go above four parameters/dimensions.
Most importantly, the user of our software does not need any statistical expertise to use it, and in theory, if automated feedback is given, the optimist may be eliminated. Because no user input is necessary between iterations, the optimization process may be completely automated. |