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[2602.19390] Artificial Intelligence for Modeling & Simulation in Digital Twins
Summary
This article explores the integration of artificial intelligence with modeling and simulation in digital twins, highlighting their roles and future research directions.
Why It Matters
Understanding the convergence of AI and modeling & simulation in digital twins is crucial for businesses aiming to leverage advanced technologies for digital transformation. This paper provides insights into how these technologies can enhance operational efficiency and decision-making processes.
Key Takeaways
- Digital twins serve as live representations of physical assets, enhancing corporate digital transformation.
- Modeling and simulation techniques are vital for the effectiveness of digital twins.
- AI enhances digital twins through advanced analytics and predictive capabilities.
- Digital twins provide platforms for training and validating AI models.
- Future research should focus on overcoming challenges in integrating AI with digital twins.
Computer Science > Artificial Intelligence arXiv:2602.19390 (cs) [Submitted on 22 Feb 2026] Title:Artificial Intelligence for Modeling & Simulation in Digital Twins Authors:Philipp Zech, Istvan David View a PDF of the paper titled Artificial Intelligence for Modeling & Simulation in Digital Twins, by Philipp Zech and 1 other authors View PDF HTML (experimental) 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: ...