[D] What are the must-have books for graduate students/researchers in Machine Learning; especially for Dynamical Systems, Neural ODEs/PDEs/SDEs, and PINNs?

I’m a graduate student working in machine learning and dynamical systems, and I’m trying to build a solid foundation (and bookshelf!) for deeper study and research. I’d love to hear what books people here consider essential or transformative when it comes to understanding both the theoretical and applied sides of ML.

I’m especially interested in recommendations that cover topics like:

• Neural ODEs/PDEs/SDEs
• Physics-Informed Neural Networks (PINNs)
• Dynamical systems modeling and simulations with ML
• Applied mathematics approaches to deep learning

That said, I’d also appreciate more general ML “classics” that every researcher should be familiar with — from theory to implementation.

If you’ve gone through a grad or research path in this area, what books (or maybe lecture notes, monographs, or papers) were game-changers for you?
Would also love to hear why you’d recommend a particular book — e.g., clarity, depth, or practical usefulness.

Thanks in advance! Hoping this thread can help others building a focused reading list too.