Artificial Intelligence for Modeling & Simulation in Digital Twins

2026-02-22

AI Summary

This study addresses the deep integration of modeling and simulation (M&S) with artificial intelligence (AI) within digital twins to enhance their intelligent prediction and autonomous decision-making capabilities. By developing a comprehensive framework that incorporates physical modeling, discrete-event, and hybrid simulation methods alongside AI-driven advanced analytics and predictive modeling, the work elucidates the bidirectional synergy between M&S and AI. It positions the digital twin as a pivotal enabling platform for this convergence, revealing its multifaceted roles across business, development, and operational contexts. The research establishes an integrated theoretical foundation, identifies critical challenges, and outlines future directions to advance more intelligent and cohesive digital twin systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The convergence of modeling & simulation (M&S) and artificial intelligence (AI) is leaving its marks on advanced digital technology. Pertinent examples are digital twins (DTs) - high-fidelity, live representations of physical assets, and frequent enablers of corporate digital maturation and transformation. Often seen as technological platforms that integrate an array of services, DTs have the potential to bring AI-enabled M&S closer to end-users. It is, therefore, paramount to understand the role of M&S in DTs, and the role of digital twins in enabling the convergence of AI and M&S. To this end, this chapter provides a comprehensive exploration of the complementary relationship between these three. We begin by establishing a foundational understanding of DTs by detailing their key components, architectural layers, and their various roles across business, development, and operations. We then examine the central role of M&S in DTs and provide an overview of key modeling techniques from physics-based and discrete-event simulation to hybrid approaches. Subsequently, we investigate the bidirectional role of AI: first, how AI enhances DTs through advanced analytics, predictive capabilities, and autonomous decision-making, and second, how DTs serve as valuable platforms for training, validating, and deploying AI models. The chapter concludes by identifying key challenges and future research directions for creating more integrated and intelligent systems.

Problem

• Digital Twins
• Modeling and Simulation
• Artificial Intelligence
• Convergence
• Intelligent Systems

Innovation

• Digital Twins
• Modeling and Simulation
• Artificial Intelligence
• Hybrid Modeling
• AI-Driven Decision Making