AI与仿真融合,重塑建模全流程。
Artificial Intelligence and Modeling & Simulation: An Overview

- AI用于辅助或替代仿真各阶段任务
- 生成式AI提升仿真数据与训练效率
- 适合跨领域研究者快速了解交汇点
人工智能(AI)与建模与仿真(M&S)正日益深度融合,反映出两个领域共同的研究需求、技术进步(如生成式AI兴起)以及数据和算力资源的快速增长。本报告系统梳理了二者交叉的多个层面:一方面,AI可支持、增强甚至替代仿真中的各个环节;另一方面,仿真可作为生成数据、训练环境和评估平台服务于AI。报告按建模流程组织,涵盖模型定义、输入建模、执行、实验、验证与确认及结果分析等阶段,并通过典型案例说明大型语言模型等技术如何改变仿真实践,同时指出当前局限与开放挑战。此外,报告提供概念路线图,帮助读者在快速演变的生态中定位自身方向。
原文摘要 · Abstract (English)
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.
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