Part 2 · Chapter 15

Integrating Machine Learning into Engineering Systems

Brings the methods together at system level, emphasizing paradigm selection, data-versus-physics tradeoffs, validation, deployment, trust, and human oversight.

Chapter map

What this chapter develops

  • Algorithms to engineering decisions
  • Paradigm selection
  • Data-versus-physics design matrix
  • Reliability and trust
  • System deployment
  • Autonomous and adaptive systems

Engineering practice

Application and Diamond Examples

  1. 01

    System-level design cases connect learning components to validation, deployment, digital twins, and governance

Companion materials

Portal resources

Chapter catalogFind this chapter’s indexed examples and related topics.
Python notebooksNo companion AE notebooks are currently assigned to this chapter.None
AE datasetsNo companion AE datasets are currently assigned to this chapter.None
DiscussionAsk chapter-specific questions and compare engineering interpretations with other readers.