# Artificial Intelligence for Modeling & Simulation in Digital Twins

arXiv: https://arxiv.org/abs/2602.19390 · HTML: https://mlsanity.com/paper/2602.19390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 95 references that are in the map.
- First posted on arXiv 22 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Digital Twin: Values, Challenges and Enablers](https://mlsanity.com/paper/1910.01719/) (2019)
- [A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects](https://mlsanity.com/paper/2301.13350/) (2023)
- [AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture](https://mlsanity.com/paper/2506.06580/) (2025)
- [The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches](https://mlsanity.com/paper/2209.12423/) (2022)
- [Digital Twin Evolution for Sustainable Smart Ecosystems](https://mlsanity.com/paper/2403.07162/) (2024)
- [Engineering Automotive Digital Twins on Standardized Architectures: A Case Study](https://mlsanity.com/paper/2508.18662/) (2025)
- [Introduction to Digital Twins for the Smart Grid](https://mlsanity.com/paper/2602.14256/) (2026)
- [Reasonable Experiments in Model-Based Systems Engineering](https://mlsanity.com/paper/2509.10649/) (2025)

## Abstract (arXiv)

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.