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

ABSTRACT: 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. Based on a practical definition of model complexity in the context of machine learning, the interplay of systematic data generation and model performance is examined considering various sources of uncertainty: this includes uncertainties caused by stochastic sampling strategies, imprecise data, suboptimal modeling, and model evaluation. Results obtained from electrical circuit models with varying complexity show that not all AL sampling strategies outperform conventi

SUBMITTER: Xu X 

PROVIDER: S-EPMC11688508 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature