← Physics-Informed Neural ODEs for IBR Dynamics

Physics-Informed Neural ODEs for IBR Dynamics

Neural Network Foundations

C3-02 · Module outline

Understand the function approximator used to learn a vector field.

Learning objectives

  • Explain an MLP forward pass and training loss.
  • Trace a simple gradient through the network.

Concept outline

  1. Features, activations, and capacity
  2. Losses, gradients, and optimization
  3. Static approximation as a bridge to dynamics

Experiment direction · Planned

Simulation and code experiment

Fit a nonlinear function and vary the hidden width.

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

Practice and discussion

Explain what the network output should represent in a dynamic model.

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