Artificial Intelligence for Modeling & Simulation in Digital Twins

• URL: http://arxiv.org/abs/2602.19390v1
• Date: Sun, 22 Feb 2026 23:47:43 GMT
• Title: Artificial Intelligence for Modeling & Simulation in Digital Twins
• Authors: Philipp Zech, Istvan David,
• Abstract summary: The convergence of modeling & simulation (M&S) and artificial intelligence (AI) is leaving its marks on advanced digital technology.<n>Pertinent examples are digital twins (DTs) - high-fidelity, live representations of physical assets, and frequent enablers of corporate digital maturation and transformation.<n>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.
• Score: 3.351714665243138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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