Adaptive Sampling for Fast and Accurate Metamodel-Based Sensitivity Analysis of Complex Electromagnetic Problems

This article presents the development of an adaptive sampling strategy for building surrogate models of complex electromagnetic systems. Accurate sensitivity analysis is crucial to electromagnetic compatibility but usually requires a few thousand calls of the numerical model if performed using classical Monte Carlo sampling. In the case of an expensive computational model, this results in extremely long computation. Hence, with only a few calls of the numerical model, surrogate models are built to approximate the behavior of the system. This accurate predictor can then be used instead of the expensive computational model for various analyses. The active learning sampling strategy has been tested successfully on a realistic finite-element method model.

Automated summary

This article presents the development of an adaptive sampling strategy for building surrogate models of complex electromagnetic systems. Accurate sensitivity analysis is crucial but computationally intensive. Therefore, a surrogate model is built using a small number of calls to approximate system behavior accurately. The active learning sampling strategy has been successfully tested on a realistic finite-element method model.