论文标题
Sobmor：基于结构化优化的模型订单降低
SOBMOR: Structured Optimization-Based Model Order Reduction
论文作者
论文摘要
旨在保留给定全订单模型（FOM）结构特征的模型订单降低（MOR）方法通常与非结构性保护相比，通常具有较低的精度。在本文中，我们提出了一个用于结构的MOR的框架，该框架允许以更高的精度计算结构化降低的订单模型（ROM）。该框架基于参数优化，即ROM的系统矩阵的元素在迭代上变化以最大程度地降低一个目标函数，以测量FOM和ROM之间的差异。结构约束可以在ROM的参数化中编码。该方法仅取决于频率响应数据，因此可以应用于广泛的动态系统。 在与其他具有结构的MOR算法进行比较的过程中，我们说明了方法对港口港口和对称的二阶系统的有效性。
Model order reduction (MOR) methods that are designed to preserve structural features of a given full order model (FOM) often suffer from a lower accuracy when compared to their non-structure-preserving counterparts. In this paper, we present a framework for structure-preserving MOR, which allows to compute structured reduced order models (ROMs) with a much higher accuracy. The framework is based on parameter optimization, i.e., the elements of the system matrices of the ROM are iteratively varied to minimize an objective functional that measures the difference between the FOM and the ROM. The structural constraints can be encoded in the parametrization of the ROM. The method only depends on frequency response data and can thus be applied to a wide range of dynamical systems. We illustrate the effectiveness of our method on a port-Hamiltonian and on a symmetric second-order system in a comparison with other structure-preserving MOR algorithms.
