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：我们提出了一种面向频域问题的非侵入式插值代理建模中的贪心采样策略。我们利用一种非侵入式且低成本的误差指标，驱动基于高保真样本的代理模型的自适应样本选择。我们构建了一个理论框架来支撑所提出的指标。此外，针对用于终止贪心采样迭代的终止准则，我们提出了多种实用方法。为展示贪心策略的有效性，我们将其与著名的Loewner框架相结合进行数值测试。为此，我们考虑了多个基准算例，结果表明自适应方法能够从少量样本中有效逼近复杂系统的传递函数。