We present an adaptive sampling strategy for the optimization-based structure preserving model order reduction (MOR) algorithm developed in [Schwerdtner, P. and Voigt, M. (2020). Structure preserving model order reduction by parameter optimization, Preprint arXiv:2011.07567]. This strategy reduces the computational demand and the required a priori knowledge about the given full order model, while at the same time retaining a high accuracy compared to other structure preserving but also unstructured MOR algorithms. A numerical study with a port-Hamiltonian benchmark system demonstrates the effectiveness of our method combined with its new adaptive sampling strategy. We also investigate the distribution of the sample points.

中文翻译：

---

哈密​​尔顿系统结构保持模型降阶的自适应采样

我们为在 [Schwerdtner, P. 和 Voigt, M. (2020) 中开发的基于优化的结构保持模型降阶 (MOR) 算法提出了一种自适应采样策略。通过参数优化降低结构保留模型阶数，预印本 arXiv:2011.07567]。这种策略减少了计算需求和所需的关于给定全阶模型的先验知识，同时与其他结构保留算法和非结构化 MOR 算法相比，保持了较高的准确性。使用 port-Hamiltonian 基准系统进行的数值研究证明了我们的方法与其新的自适应采样策略相结合的有效性。我们还调查了样本点的分布。