论文标题
部分可观测时空混沌系统的无模型预测
Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns.
