How references work in this book¶
Each chapter has its own References section at the bottom of the page, listing only the works that chapter cites. Use those lists when you study a topic; this page keeps only the few sources the whole book leans on.
All entries come from one shared bibliography (references.bib in the repository), so a source has the same citation everywhere. Missing a key paper, or found a wrong DOI? Please tell us or contribute a fix.
Core textbooks¶
| Textbook | Used for |
|---|---|
| Kundur & Malik (2022) | Power-system dynamics and transient stability |
| Brunton & Kutz (2022) | Data-driven dynamics |
| Bishop & Bishop (2024) | Neural networks, training, and backpropagation |
| Thuerey et al. (2021) | Physical losses and differentiable physics |
| Ljung (1999) | Classical estimation, excitation, and identifiability |
Chapter or section numbers are given on chapter pages only where an edition has been checked.
Simulators¶
| Tool | Role |
|---|---|
| ANDES Cui et al., 2021ANDES developers, 2026 | Network simulation reference (GPL-3.0). Course bridge: ANDES as network reference |
| ANDES Gallery | Optional cases; preferred network CCT example: Kundur CCT mapping |
Maths refresher (external, link only)¶
The EECS 245 course notes Rampure, 2026 give optional linear-algebra, gradient, and SVD background. They are CC BY-NC-ND 4.0, so this book links to them and does not adapt them. Topic table: Preparation overview.
Where this book reuses Physics-based Deep Learning¶
Thuerey et al. (2021) is a method textbook with fluid-dynamics examples. This book takes its ideas and rewrites them for the SMIB swing equation; it does not copy its figures or notebooks.
| PBDL chapter | Used in |
|---|---|
| Overview and models | SciML landscape § How tightly is physics coupled? |
| Supervised training discussion | Data and surrogates; pillar Discussion sections |
| Physical losses, discussion | Forward PINN, inverse PINN |
| Differentiable physics, DP versus PINN | Differentiable simulation; Lab 3 option |
| Improved gradients, half-inverse gradients | Automatic differentiation § Gradient scaling |
| Model reduction and time series | Dynamics overview; Lab 4 |
| Probabilistic learning, graph diffusion, reinforcement learning | Advanced overview § Fast-forward topics |
Fluids-only examples (Navier-Stokes, airfoils, Lagrangian and meshless methods, GANs) are not part of this course.
Neighbouring courses and code¶
Other open courses, tutorials, and research codes are listed on Related open resources. Research code repositories are further reading, not redistributable course material; assessed work is only Labs 1–5 under Laboratories.
- Kundur, P., & Malik, O. P. (2022). Power System Stability and Control (Second). McGraw-Hill Education. https://openlibrary.org/isbn/9781260473544
- Brunton, S. L., & Kutz, J. N. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Second). Cambridge University Press. 10.1017/9781009089517
- Bishop, C. M., & Bishop, H. (2024). Deep Learning: Foundations and Concepts. Springer. 10.1007/978-3-031-45468-4
- Thuerey, N., Holzschuh, B., Holl, P., Kohl, G., Lino, M., Liu, Q., Schnell, P., & Trost, F. (2021). Physics-based Deep Learning. arXiv. 10.48550/arXiv.2109.05237
- Ljung, L. (1999). System Identification: Theory for the User (Second). Prentice Hall PTR. https://openlibrary.org/isbn/0136566952
- Cui, H., Li, F., & Tomsovic, K. (2021). Hybrid Symbolic-Numeric Framework for Power System Modeling and Analysis. IEEE Transactions on Power Systems, 36(2), 1373–1384. 10.1109/TPWRS.2020.3017019
- ANDES developers. (n.d.). ANDES documentation. Retrieved October 14, 2026, from https://docs.andes.app/en/stable/
- Rampure, S. (n.d.). EECS 245 course notes. Retrieved October 14, 2026, from https://notes.eecs245.org/