Ideal springs are a common, simple element in this field, but this kind of spring is very much not that.
You're probably better off improving the sample-based version by fading out the audio when necessary and using different samples based on the way it's triggered. If you have "ultra-dry" samples (maybe taken with a contact mic), you can add a convolution effect with a well-chosen impulse response, this will allow you to sharply cut off or adjust the audio and still have a natural-sounding tail.
If you don't mind humoring me (I'm quite the novice in this field), if I automated the recording of "all" possible positions for a spring (say I had a motor positioned in a way that would let me pull the spring in any polar direction), would that make modeling potentially easier?
There might be a "train an AI, here's 1000 recordings" angle, but I'm not necessarily interested in/asking about that.
Just strictly for modeling, would it help the R&D phase to have a lot of high sample rate recordings? Thanks a lot!
P.S. Also, if you have a good intro to DSP class/book, I'd love to hear it. I know about a few, but a recc is always appreciated
I don't think that recording a large number of starting positions would help that much with creating a (non-ML) model, and I doubt a high sample rate would provide much useful information either. A more common approach would be to try getting separate sounds for the impulse and the resonant body, though they may be impossible to really separate, and the actual model may end up more complex than that.
You probably have a good starting point already with your code for the animated model. I think the sound mostly comes from the collision between coils (collisions not visible in your animated model), and almost entirely from the lowest couple of windings that are against the wall. This is your impulse. The resonant body might be in 2 parts: the wall and the long end of the spring. Your existing model can tell you when to trigger the impulses, and how much force to put into them.
For resources, one of my favorite intros to DSP is the one by Sean Luke: https://cs.gmu.edu/~sean/book/synthesis/
I wrote my own intro to physical modeling, though it focuses on different instruments: https://www.osar.fr/notes/waveguides/
Julius O. Smith has an encyclopedic amount of content on the topic, though it's often condensed into math that can be hard to apply: https://ccrma.stanford.edu/~jos/