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Physics-Informed Neural ODEs for IBR Dynamics

Stability Diagnostics for Learned Models

C3-08 · Module outline

Check equilibria, local modes, and finite-horizon recovery before making claims.

Learning objectives

  • Separate sampled recovery from an asymptotic stability certificate.
  • Check how learned residuals change equilibrium and damping.

Concept outline

  1. Learned equilibria and Jacobians
  2. Energy diagnostics and soft regularization
  3. Horizon sensitivity and retained failures

Experiment direction · Planned

Simulation and code experiment

Compare recovery maps at multiple horizons and inspect disagreement cases.

Model explanations, parameter settings, runnable code, and result interpretation will be developed here.

Practice and discussion

State exactly what a sampled stability penalty establishes.

Worked solutions and feedback will accompany the full lesson.

Material preparation

Related notes, models, or notebooks are available and need adaptation into a web lesson and experiment.

Adapt the existing material into a lesson, a reproducible experiment, and worked practice.

All modules in this course
  1. Dynamical Systems and Trajectory Data
  2. Neural Network Foundations
  3. Neural ODEs and Learned Vector Fields
  4. Derivative and Trajectory Matching
  5. Physics Constraints and Residual Backbones
  6. Continuous-Time PINNs and Neural ODEs
  7. Parameter Identification and Generalization
  8. Stability Diagnostics for Learned Models
  9. From Swing Models to Converter Dynamics