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：我们针对频域问题的非侵入式插值代理建模提出了一种贪心采样策略。该方法利用非侵入式且计算成本低廉的误差指标驱动高保真样本的自适应选择，从而构建代理模型。我们建立了支撑该指标的理论框架，同时提出了多种用于终止贪心采样迭代的实用准则。为验证该贪心策略的有效性，我们将其与著名的Loewner框架结合进行了数值测试。通过多个基准算例的验证，结果表明这种自适应方法能够基于少量样本有效逼近复杂系统的传递函数。