论文标题
库普曼（Koopman）以置信度降低了订单建模
Koopman Reduced Order Modeling with Confidence Bounds
论文作者
论文摘要
本文基于Koopman操作员理论介绍了一种简化的订单建模技术，该技术给出了模型预测的置信度。它基于Koopman操作员的数据驱动的光谱分解。减少的顺序模型是使用有限数量的Koopman特征值和模式构建的，而其余的频谱则被视为噪声过程。此噪声过程用于提取置信度范围。此外，我们提出了一种启发式算法，以选择要保留模型的确定性模式的数量。
This paper introduces a reduced order modeling technique based on Koopman operator theory that gives confidence bounds on the model's predictions. It is based on a data-driven spectral decomposition of the Koopman operator. The reduced order model is constructed using a finite number of Koopman eigenvalues and modes, while the rest of spectrum is treated as a noise process. This noise process is used to extract the confidence bounds. Additionally, we propose a heuristic algorithm to choose the number of deterministic modes to keep in the model.
