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Physics-Informed Neural ODEs for IBR Dynamics

Parameter Identification and Generalization

C3-07 · Module outline

Separate unknown-parameter estimation from prediction at supplied parameters.

Learning objectives

  • Recognize compensation between parameters and residuals.
  • Evaluate interpolation and extrapolation separately.

Concept outline

  1. Grey-box derivative matching
  2. Joint and staged fitting assumptions
  3. Held-out trajectories and parameter cases

Experiment direction · Planned

Simulation and code experiment

Compare physical-parameter estimates and held-out predictions across model forms.

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

Practice and discussion

Explain why a small loss need not identify the true parameters.

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