论文标题
索引索引一个港口 - 哈米尔顿描述符系统的结构保存模型订单降低
Structure-Preserving Model Order Reduction for Index One Port-Hamiltonian Descriptor Systems
论文作者
论文摘要
我们开发了基于优化的结构传播模型订购降低（MOR）方法，用于分化指数的港口 - 哈米尔顿港（PH）描述符系统。 pH形式的描述器系统允许基于能量的建模和跨不同物理域，尺度和精度的物理系统的直观耦合。这使PH模型非常适合大型系统网络的组件建模。在这种情况下，通常有必要在MOR期间保留pH结构。我们讨论了针对pH系统的当前基于投影和结构的MOR算法，并为该任务提供了新的基于优化的框架。我们方法的好处包括对代数约束的简化处理，并且通常会更高的降级模型准确性，这是通过几个数值示例证明的。
We develop optimization-based structure-preserving model order reduction (MOR) methods for port-Hamiltonian (pH) descriptor systems of differentiation index one. Descriptor systems in pH form permit energy-based modeling and intuitive coupling of physical systems across different physical domains, scales, and accuracies. This makes pH models well-suited building-blocks for component-wise modeling of large system networks. In this context, it is often necessary to preserve the pH structure during MOR. We discuss current projection-based and structure-preserving MOR algorithms for pH systems and present a new optimization-based framework for that task. The benefits of our method include a simplified treatment of algebraic constraints and often a higher accuracy of the resulting reduced-order model, which is demonstrated by several numerical examples.
