My current area of research is in sparse, event-based encodings of musical audio (https://blog.cochlea.xyz/sparse-interpretable-audio-codec-pa...). I'm very interested in decomposing audio signals into a description of the "system" (e.g., room, instrument, vocal tract, etc.) and a sparse "control signal" which describes how and when energy is injected into that system. This toy was a great way to start learning about physical modeling synthesis, which seems to be the next stop in my research journey. I was also pleasantly surprised at what's possible these days writing custom Audio Worklets!
Now that I understand the basics of how this works, I'd like to use a (much) more efficient version of the simulation as an infinite-dataset generator and try to learn a neural operator, or NERF like model that, given a spring mesh configuration, a sparse control signal, and a time, can produce an approximation of the simulation in a parallel and sample-rate-independent manner. This also (maybe) opens the door to spatial audio, such that you could approximate sound-pressure levels at a particular point in time _and_ space. At this point, I'm just dreaming out-loud a bit.