A methodology for parameter estimation in system dynamics models using artificial intelligence

IF 1.6 3区工程技术 Q4 ENGINEERING, INDUSTRIAL

Systems Engineering Pub Date : 2023-09-16 DOI:10.1002/sys.21718

Jyotirmay Gadewadikar, Jeremy Marshall

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引用次数: 1

Abstract

Abstract Multiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests.

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基于人工智能的系统动力学模型参数估计方法

摘要系统动力学模型参数的单独仿真和估计工具有多种。人工智能(AI)越来越多地用于估计系统动力学模型的参数。建模工具和高级环境的发展给整个社区带来了巨大的利益。将人工智能工具整合到系统动力学中，为扩展当前的决策方法提供了机会。随着系统变得复杂，纳入基于证据的数据驱动方法的需求也在增加。通过集成系统动力学工具，促进人工智能和系统动力学在集成环境下的仿真，可以利用最新的数据估计模型参数，有效保持模型的完整性。这为系统动力学模型及其分析的效率和能力提供了优势。本文提出了一种将回归人工智能纳入系统动力学模型进行仿真和分析的一般方法。为了证明该方法的有效性，使用支持向量机(svm)、人工神经网络(ann)和随机森林进行了一个涉及易感-感染-恢复模型和COVID - 19大流行经验数据的案例研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Systems Engineering 工程技术-工程：工业

CiteScore

5.10

自引率

20.00%

发文量

审稿时长

6 months

期刊介绍： Systems Engineering is a discipline whose responsibility it is to create and operate technologically enabled systems that satisfy stakeholder needs throughout their life cycle. Systems engineers reduce ambiguity by clearly defining stakeholder needs and customer requirements, they focus creativity by developing a system’s architecture and design and they manage the system’s complexity over time. Considerations taken into account by systems engineers include, among others, quality, cost and schedule, risk and opportunity under uncertainty, manufacturing and realization, performance and safety during operations, training and support, as well as disposal and recycling at the end of life. The journal welcomes original submissions in the field of Systems Engineering as defined above, but also encourages contributions that take an even broader perspective including the design and operation of systems-of-systems, the application of Systems Engineering to enterprises and complex socio-technical systems, the identification, selection and development of systems engineers as well as the evolution of systems and systems-of-systems over their entire lifecycle. Systems Engineering integrates all the disciplines and specialty groups into a coordinated team effort forming a structured development process that proceeds from concept to realization to operation. Increasingly important topics in Systems Engineering include the role of executable languages and models of systems, the concurrent use of physical and virtual prototyping, as well as the deployment of agile processes. Systems Engineering considers both the business and the technical needs of all stakeholders with the goal of providing a quality product that meets the user needs. Systems Engineering may be applied not only to products and services in the private sector but also to public infrastructures and socio-technical systems whose precise boundaries are often challenging to define.