EAGER: A Categorical Approach to Systems Modeling for Systems Engineering

EAGER：系统工程系统建模的分类方法

• 批准号: 1746077
• 负责人: Eswaran Subrahmanian
• 金额: $ 18.62万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1746077&HistoricalAwards=false
• 关键词: EAGER; Categorical; Approach; Systems; Modeling

This EArly-concept Grant for Exploratory Research (EAGER) grant supports fundamental research into the use of Category Theory (CT) to create a mathematical framework for systems performance modeling in development of large engineered systems. Accurate and effective modeling of system performance is critical to inform decision making in systems engineering projects. A variety of incommensurable models (physical, logical and human) of system components and subsystems are relevant to understanding the system as a whole. A major shortfall of current practice is an inability to compose models to simulate and study system behavior seamlessly at different levels of abstraction using different formalisms including differential equations, logic and stochastic models. This project will investigate a new approach based on CT that promises to address this shortcoming and transform the practice of systems engineering. Better predictions of system performance will lead to better-performing engineered systems in areas that include, but are not limited to, aerospace, healthcare, defense, and energy. The technical advances of this project will be demonstrated specifically in the design of stable smart power distribution system.Current systems engineering practice relies on information models such as SysML, BPML and other languages to organize the numerous models involved in systems engineering and the data they generate. The objective of this project is to demonstrate CT as a bridging meta-formalism across the variety of mathematical formalisms used in the modeling of systems. This research will contribute fundamental results in CT-based systems modeling that will enable new tools and approaches for systems engineering. The formal language afforded by CT can transcend the individual modeling formalisms and offer systems engineers the ability to create a compositional model of the system for inferencing across scales. The smart power distribution system problem exhibits many challenging aspects, including stochasticity (in power loading and generation) and operation that must consider multiple time scales (milliseconds, minutes, and hours). Success on this testbed problem will provide compelling evidence that further study of CT-based systems modeling is warranted.

这项早期概念探索性研究资助 (EAGER) 资助支持使用类别理论 (CT) 的基础研究，为大型工程系统开发中的系统性能建模创建数学框架。准确有效的系统性能建模对于系统工程项目中的决策制定至关重要。系统组件和子系统的各种不可通约的模型（物理的、逻辑的和人类的）与理解整个系统有关。当前实践的一个主要缺陷是无法使用不同的形式（包括微分方程、逻辑和随机模型）构建模型来在不同抽象级别无缝地模拟和研究系统行为。该项目将研究一种基于 CT 的新方法，有望解决这一缺点并改变系统工程的实践。对系统性能的更好预测将导致工程系统在包括但不限于航空航天、医疗保健、国防和能源等领域表现更好。本项目的技术先进性将具体体现在稳定的智能配电系统的设计上。当前的系统工程实践依靠SysML、BPML等语言的信息模型来组织系统工程涉及的众多模型及其生成的数据。 该项目的目标是证明 CT 作为跨系统建模中使用的各种数学形式主义的桥梁元形式主义。这项研究将为基于 CT 的系统建模贡献基础成果，从而为系统工程提供新的工具和方法。 CT 提供的形式语言可以超越单独的建模形式，并使系统工程师能够创建系统的组合模型以进行跨尺度的推理。智能配电系统问题表现出许多具有挑战性的方面，包括随机性（电力加载和发电）以及必须考虑多个时间尺度（毫秒、分钟和小时）的操作。该测试台问题的成功将提供令人信服的证据，证明有必要进一步研究基于 CT 的系统建模。