How the notes are organized. Use the left sidebar for day-to-day navigation; this page is the big picture. For every chapter and lab on one page, see the Full outline.
| Section | What you find here |
|---|---|
| Front matter | Start here, how to use, local preview, running notebooks, your first lab, this map, full outline, learning paths, related open resources, contribute, status, feedback, license |
| Preparation | Overview, three bridge primers (programming, power systems for ML, ML for power systems), plus four diagnostic notebooks (stubs today) |
| Foundations | Shared SMIB model and core SciML ideas (losses, forward/inverse PINN, baselines) |
| Dynamics | Deeper dynamic modeling and simulation (SciML for trajectories / vector fields) |
| Stability | Stability assessment (labels, CCT, surrogate stability; ANDES network bridge after SMIB) |
| Identification | Parameter estimation and system identification |
| Advanced topics | Synthesis (assessment → decision) plus optional extensions (operators, UQ, converters) |
| Laboratories | Assessed Labs 1–5 |
| Reference | Reading list, glossary, and notation |
Read Preparation → Foundations, then deepen along Dynamics, Stability, or Identification, when you want the long route. Other entrances (by background and by role) are on Start here and Learning paths. Any chapter can be a starting page when its Before you start box is satisfied. Labs sit beside the notes and cross-link from chapters.
Methods appear when an application needs them. PINN is an early deep path (Labs 2–3); other SciML families sit under Lab 4, Identification options, or Advanced. Labs 1–5 are the fixed assessed set — deepen notebooks rather than adding Lab 6. Reader summary: How SciML methods appear.
This top-level layout is intentional and meant to stay stable as the notes grow: shared foundations, three applications, labs beside the prose, and a separate reference section. Semester week-by-week paths belong under Learning paths, not as a rewrite of these sections.
What each application produces¶
| Application | Engineering question | What you produce |
|---|---|---|
| Dynamic modeling and simulation | How will the system evolve under specified inputs, parameters, and disturbances? | State trajectories, learned vector fields, hybrid dynamic models, parameterized response maps |
| Stability assessment | Will the system remain stable, and how close is it to a stability boundary? | Stable/unstable decisions, critical clearing time (CCT), empirical stability boundaries |
| Parameter estimation and system identification | Which parameters and dynamic relationships explain the measurements? | Inertia/damping estimates, controller parameters, learned missing terms, uncertainty estimates |
In the sidebar these appear as Dynamics, Stability, and Identification.