ML4EA

Part 2 · Chapter 13

Physics-Informed Machine Learning

Combines governing equations, constraints, data, and learning algorithms to build models that respect engineering knowledge and limited-data conditions.

Chapter map

What this chapter develops

  • Governing equations and constraints
  • Physics-augmented losses
  • Physics-informed neural networks
  • Hybrid and grey-box models
  • Operator learning
  • Validation and deployment

Engineering practice

Application and Diamond Examples

  1. 01

    Physics-constrained capacitor discharge regression

  2. 02

    Mass-spring fault classification

  3. 03

    PINN solution of a one-dimensional PDE

Companion materials

Portal resources

Chapter catalogFind this chapter’s indexed examples and related topics.Application Example catalogOpen 3 validated notebooks with a verified portal account.AE datasetsReview dataset sources and generation details for this chapter's Application Examples.DiscussionAsk chapter-specific questions and compare engineering interpretations with other readers.