Model maturity towards modeling and simulation:Concepts,index system framework and evaluation method

Simulation has become an essential way and sometimes the only way to study complex systems(e.g.,system of systems,SoS).Simulation is the model-based activity.How to build a high-quality model is the first consideration in simulation.Fidelity and credibility are the two mostly used metrics to evaluate the quality of a model.However,the definitions and evaluation methods of fidelity and credibility vary from one research to another and it’s hard to evaluate the metrics precisely.More importantly,the evolution process of a model in use cannot be directly reflected by the two metrics.Therefore,this paper introduces the model maturity to track the status of a model during its life cycle,especially in the use and management phases,which will be an important supplement to the quality evaluation system of models.The concept of model maturity is given and a framework of index system for model maturity evaluation is established.Then,a hierarchical evaluation method based on qualitative and quantitative analysis(HEQQ)for model maturity is proposed.Finally,a case study is used to validate the feasibility and effectiveness of the proposed method.

XDEVS: A hybrid system modeling framework

When Discrete Event System Specification(DEVS)is used as a modeling tool,there is a semantic gap between a DEVS model and the mathematical representation,which may result in understanding difficulties.To provide a more intuitive form of modeling,XDEVS expands the concept of states in DEVS.The continuous state is introduced in XDEVS,which enhances the ability to model hybrid systems.Based on the DEVS simulation framework,a simulation engine is developed to drive the XDEVS model safely and efficiently and avoid the wrong location of state events during the simulation of the continuous model.A hybrid model is constructed and simulated using XDEVS.A comparison between the XDEVS model and models described by DEV&DESS and GDEVS shows that XDEVS can clearly express the structure of the model and reduce the burden on modelers.

CoSMSOL:Complex system modeling,simulation and optimization language

Complex System Modeling,Simulation and Optimization Language(CoSMSOL)is problem-oriented and designed to run on multi-core computers.This paper provides the system environment of CoSMSOL and proposes the modeling methods of complex system,language text specification,function library,algorithm library,parallel simulation algorithms and intelligent optimization algorithms which support continuous system,discrete system and agent systems.Also,we developed a simulation language compiler of CoSMSOL,which is employed in two case studies generating a multi-entity war gaming system and an aerodynamic spacecraft model.The two cases illustrate main functions and implementation processes of CoSMSOL.The results validate that CoSMSOL is useful to model agent-based system and aerospace system.

Research on the dynamic management of cloud simulation derived data

Cloud simulation derived data is defined as the data related to service version,characteristics,relationships,runtime environments and cross-domain communication during service execution in cloud simulation environment,collectively.It is of great value and significance in cloud simulation for service description,service composition and resource management.The types of derived data are abundant and the amount of it is huge.Existing studies on cloud simulation usually assume all of the derived data required for a specific task is well organized and available anytime,which is of course impossible.Derived data needs to be expressed and managed in terms of knowledge to make sure the smooth execution of cloud simulation platform.Therefore,this paper presents a derived data management method in cloud simulation platform to enable derived data collection and dynamic knowledge storage.The prototype system of the proposed method are established and a virtual prototype of double girder crane is taken as an example to verify the effectiveness of the method.

工业大模型:体系架构、关键技术与典型应用

随着新一代人工智能的发展,大模型(如GPT-4o等)凭借大规模训练数据、网络参数和算力涌现出强大的生成能力、泛化能力和自然交互能力,展现出改变工业世界的巨大潜力.尽管大模型已在自然语言等多个领域取得突破性进展,但其在工业应用中的探索仍处于初级阶段,当前工业大模型的系统性研究仍属空白.工业应用中特有的异质数据模态、复杂多样的专业化场景、长流程的关联性决策、以及对于可信性实时性的高要求,使得通用大模型无法直接用于解决复杂的工业问题,亟需开展全新的工业大模型基础理论和关键技术研究.本文系统地探讨了工业大模型的挑战问题、概念内涵、体系架构、构建方法、关键技术与典型应用.从5个挑战问题的分析出发,提出了工业大模型的全新定义和体系架构;同时,提出了工业大模型的四阶段构建方法,阐述了工业大模型核心关键技术;然后,基于所提出的工业大模型6种核心应用能力,探讨了面向产品全生命周期的工业大模型典型应用场景,并给出了“基石”工业大模型原型系统在生成式人工智能方面的应用实例;最后,探讨和展望了工业大模型未来的研究方向和开放性问题.本文将为工业大模型这一全新研究方向的开辟与发展,提供基础理论、关键技术和行业应用的全面指导.