论文标题
量化不确定性的多目标成本
Quantifying the multi-objective cost of uncertainty
论文作者
论文摘要
各种现实世界的应用程序涉及建模具有巨大不确定性的复杂系统，并根据不确定模型优化多个目标。量化模型不确定性对给定操作目标的影响对于设计最佳实验至关重要，这些实验可以最有效地降低影响与手头应用有关的目标的不确定性。在本文中，我们提出了不确定性的平均多目标成本（多目标MOCU）的概念，该概念可用于基于目标的不确定系统的不确定性量化，以考虑多个操作目标。我们提供了几个说明性的例子，这些例子证明了所提出的多目标MOCU的概念和优势。此外，我们提出了一个基于哺乳动物细胞周期网络的现实世界示例，以证明如何使用多个目标不确定性的操作影响，当时有多个（可能是竞争的目标）。
Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.
