论文标题
基于广义内核的动态模式分解
Generalized Kernel-Based Dynamic Mode Decomposition
论文作者
论文摘要
在高维繁殖的内核希尔伯特空间中减少的建模为有效的非线性动力学提供了机会。在这项工作中，我们根据低等级约束优化和基于内核的计算设计了一种算法，该算法概括了一种称为“基于内核的动态模式分解”的方法。该新算法的特征是数值模拟和计算复杂性证明的近似准确性的增益。
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based computation that generalizes a recent approach called "kernel-based dynamic mode decomposition". This new algorithm is characterized by a gain in approximation accuracy, as evidenced by numerical simulations, and in computational complexity.
