Artificial Intelligence for Modeling & Simulation in Digital Twins

TL;DR

This paper explores how artificial intelligence enhances digital twins through advanced analytics and autonomous decision-making, and how digital twins serve as platforms for AI training and validation, advancing integrated intelligent systems.

Contribution

It provides a comprehensive analysis of the interplay between AI, modeling & simulation, and digital twins, highlighting their roles and integration in modern digital technology.

Findings

AI improves digital twins with predictive analytics and autonomy

Digital twins act as platforms for AI model training and validation

The chapter identifies challenges and future directions for integrated systems

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.