8 Integrating inference and intelligence
This chapter covers
- The importance of a solid data foundation for intelligence
- Detecting anomalies and faults
- Forecasting the future with classic machine learning approaches
- Generative and agentic AI
Visualizing the data we have collected about the physical system our digital twin represents enabled us to build descriptive and informative capabilities, allowing us to see the system’s current state and use that information to make better decisions. In this chapter, we look at how you can integrate AI and ML into your digital twin to extend it toward diagnostic, predictive, and decision-support capabilities.
We have looked at how to sense changes in a physical system, communicate and store them, and build a digital representation of reality through data contextualization. This solid data foundation is absolutely essential before considering how to incorporate AI/ML. Historical data must be reliable, well-structured, and contextualized before models built on top of it can be trusted to anticipate future states of a physical system.
We begin by looking at classical AI/ML techniques, including supervised and unsupervised learning, and explore how they can be applied to different types of data stored in a digital twin. We then explore generative AI and agentic architectures that extend these capabilities into systems that can observe, reason, plan, and execute actions with minimal human intervention.