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Artificial Intelligence for Modeling & Simulation in Digital Twins

Philipp Zech, Istvan David

TL;DR

This chapter investigates 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.

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.

Artificial Intelligence for Modeling & Simulation in Digital Twins

TL;DR

This chapter investigates 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.

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.
Paper Structure (43 sections, 4 figures)

This paper contains 43 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Foundations of Digital Twins
  3. Key Components of Digital Twins
  4. Physical asset: The real-world entity
  5. Virtual model: The simulation-based representation
  6. Data layer: Analytics, machine learning, and automation
  7. Connectivity & synchronization: Real-time data exchange
  8. The Roles of Digital Twins
  9. Operation
  10. Development
  11. Business
  12. The Synergy of M&S and AI in DTs
  13. M&S in Digital Twins
  14. Role and purpose of M&S
  15. Integration of M&S within the digital twin framework
  16. ...and 28 more sections

Figures (4)

  • Figure 1: The key components of a DT include the physical asset and virtual model, which exchange data via the connectivity & synchronization layer. On top of this, the data layer manages data collected from both the physical asset and from simulations of the virtual model.
  • Figure 2: Digital assets classified by the level of automation in data exchange with the corresponding physical asset, in accordance with kritzinger2018digital
  • Figure 3: Many views of the same concept: DTs play various operational, business, and development roles in companies, providing a frequent source of misunderstandings due to different interpretations
  • Figure 4: Typical roles of AI in the different views of DTs