Artificial Intelligence and Modeling & Simulation: An Overview

2026-07-31

AI Summary

This study addresses the lack of systematic synthesis at the intersection of artificial intelligence (AI) and modeling and simulation (M&S) by proposing, for the first time, a structured framework based on the full M&S lifecycle—encompassing model construction, input modeling, execution, experimentation, validation, and output analysis. It elucidates the bidirectional integration mechanisms between AI and simulation: how AI enhances or substitutes traditional simulation components, and how simulation supports AI training and evaluation. Incorporating generative AI technologies such as large language models, the paper identifies representative application paradigms and integration approaches across each phase, synthesizes key achievements, and presents a conceptual roadmap tailored to the rapidly evolving ecosystem, while highlighting current limitations and open research challenges.

Abstract

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.

Problem

• Artificial Intelligence
• Modeling & Simulation
• Generative AI
• Large Language Models
• Interdisciplinary Integration

Innovation

• Artificial Intelligence
• Modeling & Simulation
• Generative AI
• Large Language Models
• Simulation-AI Integration