We propose a strategy for greedy sampling in the context of non-intrusive interpolation-based surrogate modeling for frequency-domain problems. We rely on a non-intrusive and cheap error indicator to drive the adaptive selection of the high-fidelity samples on which the surrogate is based. We develop a theoretical framework to support our proposed indicator. We also present several practical approaches for the termination criterion that is used to end the greedy sampling iterations. To showcase our greedy strategy, we numerically test it in combination with the well-known Loewner framework. To this effect, we consider several benchmarks, highlighting the effectiveness of our adaptive approach in approximating the transfer function of complex systems from few samples.

翻译：我们提出了一种在频域问题中基于非侵入式插值的替代模型构建中的贪心采样策略。该方法依赖一种非侵入式且计算成本低廉的误差指示器，以驱动高保真样本的自适应选择，进而构建替代模型。我们建立了支持所提指示器的理论框架，并提出了多种用于终止贪心采样迭代的实用判据。为验证贪心策略的有效性，我们将其与著名的Loewner框架结合进行数值测试。通过多个基准算例，我们证明了该自适应方法能够从少量样本中高效近似复杂系统的传递函数。