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Data-driven order reduction in Hammerstein–Wiener models of plasma dynamics
Engineering Applications of Artificial Intelligence ( IF 8 ) Pub Date : 2021-02-08 , DOI: 10.1016/j.engappai.2021.104180
Angelo Giuseppe Spinosa , Arturo Buscarino , Luigi Fortuna , Matteo Iafrati , Giuseppe Mazzitelli

The problem of identifying and therefore modelling a complex system makes use of various techniques and strategies whose computational efforts change drastically. It is not straightforward to analyse the complexity of a system as a whole because of myriads of factors, such as the way of arranging its constituent items and how they interact mutually. Intuitively, the bigger the set of sub-parts is, the more numerous the degrees of freedom are. Additionally there is not a specific and global criterion for optimally determining an always-working method that makes the identification procedure easier, especially in those contexts where the number of unknown variables can make the difference. In this sense, plasma physics is not an exception, being a field where complex phenomena, such as plasma instabilities, easily arise. From a systemic, high-level perspective, the possibility of employing a model that can describe these behaviours is particularly appealing, since it can be exploited for control applications that have not to neglect the underlying physical nature. So far, most of the work published in literature has focused on more physically-grounded models, which could describe how plasma physics works in detail, but very little has been done as mentioned before, with the aim of providing a computational, yet system-oriented, insight of these physical systems. Starting from real flux measurements recorded thanks to suitable sensors installed inside Tokamak machines, the paper attempts to provide a solution based on already known tools available in literature to solve the aforementioned problem, by combining both machine learning-based strategies for dimensionality reduction and control theory. More in detail, the whole architecture presented in this work is founded on the use of auto-encoders, which are intrinsically capable of compressing input features thanks to their structure, and Hammerstein–Wiener models, which are structurally endowed with both linear and non-linear sub-modellers for better capturing the whole dynamics to identify. By merging these functional blocks, it is possible to address both the issue of establishing the most relevant sub-set of variables for identification and the identification problem itself, resulting in a fully customisable approach to data-driven modelling.



中文翻译:

Hammerstein-Wiener等离子体动力学模型中的数据驱动的阶降

识别复杂系统并对其进行建模的问题利用了各种技术和策略,其计算工作量发生了巨大变化。由于各种因素(例如安排其组成项目的方式以及它们如何相互作用),分析整个系统的复杂性并不容易。直观地讲,子部分集越大,自由度就越多。另外,还没有一个专门的全局标准来最佳地确定一种始终有效的方法,该方法使识别过程更加容易,尤其是在未知变量数量可能有所不同的情况下。从这个意义上说,等离子体物理学并不是一个例外,因为在该领域中很容易出现诸如等离子体不稳定性之类的复杂现象。从系统的角度来看 从高层次的角度来看,采用可以描述这些行为的模型的可能性特别吸引人,因为可以将其用于不必忽略基础物理性质的控制应用程序。到目前为止,文献中发表的大部分工作都集中在更物理基础的模型上,这些模型可以描述等离子体物理学的详细工作原理,但是如前所述,它几乎没有做过任何工作,目的是提供一种可计算但又系统的系统。面向,了解这些物理系统。得益于Tokamak机器内部安装的合适的传感器,从记录的实际磁通量测量开始,本文尝试提供基于文献中已知工具的解决方案来解决上述问题,通过结合基于机器学习的降维策略和控制理论。更详细地讲,本文中介绍的整个体系结构是基于自动编码器的使用而建立的,自动编码器由于其结构而固有地能够压缩输入特征,而Hammerstein-Wiener模型在结构上又具有线性和非线性特性。线性子建模器,可以更好地捕获整个动力学以进行识别。通过合并这些功能块,既可以解决建立最相关的变量子集以进行识别的问题,也可以解决识别问题本身,从而为数据驱动的建模提供了完全可定制的方法。归功于它们的结构,它们本质上能够压缩输入特征,而Hammerstein-Wiener模型则在结构上具有线性和非线性子建模器,可以更好地捕获整个动态以进行识别。通过合并这些功能块,既可以解决建立最相关的变量子集以进行识别的问题,也可以解决识别问题本身,从而为数据驱动的建模提供了完全可定制的方法。归功于它们的结构,它们本质上能够压缩输入特征,而Hammerstein-Wiener模型则在结构上具有线性和非线性子建模器,可以更好地捕获整个动态以进行识别。通过合并这些功能块,既可以解决建立最相关的变量子集以进行识别的问题,也可以解决识别问题本身,从而为数据驱动的建模提供了完全可定制的方法。

更新日期:2021-02-08
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