发表机构
Technische Universität Ilmenau; Old Dominion University; McMaster University（伊尔默瑙工业大学; 奥多明尼昂大学; 麦克马斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本报告概述人工智能与建模及仿真的交叉领域，阐述二者双向促进关系，沿建模及仿真各阶段展示大语言模型等技术的应用，提供概念路线图助力领域探索。
AI 中文摘要
人工智能（AI）与建模及仿真（M&S）正日益紧密结合，反映了两个领域研究需求的趋同、生成式AI等技术的快速发展，以及数据与计算资源可用性的提升。本报告对AI与M&S的交叉领域进行结构化概述，二者关系是双向的：AI可支持、增强甚至替代仿真研究的部分组件，而仿真可作为AI的数据生成器、训练环境和评估平台。我们沿M&S的各阶段（从模型规范、输入建模到执行、实验、验证与确认，再到输出分析）组织这一领域，各阶段的精选研究展示了大语言模型等技术如何重塑仿真实践，同时突出了局限性与开放挑战。本报告还提供了概念路线图，帮助读者在快速变化的生态系统中定位方向。
英文摘要
Artificial intelligence (AI) and Modeling & Simulation (M&S) are increasingly intertwined, reflecting converging research needs across both communities, rapid technological advances such as the rise of generative AI, and the growing availability of data and computational resources. This report provides a structured overview of the intersections of AI and M&S. The relationship goes both ways: AI can support, augment, or even replace components of simulation studies, while simulations can serve as data generators, training environments, and evaluation platforms for AI. We organize this landscape along the stages of M&S from model specification and input modeling to execution, experimentation, verification and validation, and output analysis. Selected studies at each stage illustrates how techniques such as Large Language Models have reshaped simulation practices, while highlighting limitations and open challenges. This report also provides a conceptual roadmap that helps readers navigate a rapidly changing ecosystem.
