Description

This Master Prompt focuses on the transition from conventional DOE, in which most experimental runs are specified before experimentation begins, toward sequential and adaptive experimentation in which information obtained from completed experiments determines which experiment should be performed next. The central principle is iterative learning: conduct an initial set of informative experiments, update a statistical or machine-learning representation of the system, quantify what is known and uncertain, select the next experiment according to an explicit decision criterion, obtain the new response, update the model, and repeat until the scientific, optimization, boundary-learning or resource objective is satisfied.