Artificial Intelligence for Modeling & Simulation in Digital Twins
arXiv:2602.19390v1 Announce Type: new 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 prov
arXiv:2602.19390v1 Announce Type: new 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.
Executive Summary
The article explores the convergence of modeling & simulation (M&S) and artificial intelligence (AI) in digital twins (DTs), highlighting their complementary relationship and potential to drive corporate digital transformation. It provides a comprehensive overview of DTs, M&S, and AI, discussing their key components, roles, and applications. The article also identifies challenges and future research directions for creating more integrated and intelligent systems, emphasizing the need for advanced analytics, predictive capabilities, and autonomous decision-making.
Key Points
- ▸ The convergence of M&S and AI in DTs is driving digital transformation
- ▸ DTs integrate various services and enable AI-enabled M&S
- ▸ M&S plays a central role in DTs, with physics-based, discrete-event, and hybrid approaches
Merits
Comprehensive Overview
The article provides a thorough examination of DTs, M&S, and AI, offering a solid foundation for understanding their relationships and applications.
Demerits
Limited Discussion of Challenges
The article identifies key challenges but does not delve deeply into potential solutions or mitigation strategies, leaving some areas open for further exploration.
Expert Commentary
The article provides a valuable contribution to the understanding of DTs, M&S, and AI, highlighting the complex interplay between these technologies. However, further research is needed to fully realize the potential of these innovations, particularly in addressing the challenges of scalability, interpretability, and trustworthiness. As DTs and AI continue to evolve, it is essential to consider the broader societal implications, including the potential impact on employment, education, and social inequality.
Recommendations
- ✓ Invest in interdisciplinary research initiatives to develop more integrated and intelligent DTs and AI-enabled M&S
- ✓ Establish clear regulatory frameworks and industry standards for the development and deployment of DTs and AI