面向最小采样的认证贪心Loewner框架
Toward a certified greedy Loewner framework with minimal sampling
AI总结：
本文提出一种基于低成本误差指示器的贪心采样策略，结合Loewner框架，以少量高保真样本高效逼近复杂系统传递函数。
摘要：
我们提出了一种在频域问题中基于非侵入式插值的代理建模背景下进行贪心采样的策略。我们依赖一个非侵入式且低成本的误差指示器来驱动高保真样本的自适应选择，代理模型正是基于这些样本构建的。我们建立了一个理论框架来支撑所提出的误差指示器。我们还提出了几种用于终止贪心采样迭代的终止准则的实用方法。为了展示我们的贪心策略，我们将其与著名的Loewner框架结合进行了数值测试。为此，我们考虑了多个基准测试，突出显示了我们的自适应方法在仅用少量样本逼近复杂系统传递函数方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
