AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models.

Design of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. This paper introduces a workflow for conducting DOE comparative studies using automated machine learning.