I know that nowadays there are some physics engines pretty advanced, capable of very complex simulations.
Are we at a point in technology where if, for example, we were to simulate a rock being dropped on the floor from a certain distance, the simulation can calculate the shape and weight of the rock , the air resistance experienced during the fall, the density of the floor where the rock will fall onto, and all the other thousands of factors involved, and from those things “calculate” the sound that the rock will make when hitting the floor, and then reproduce it?
Is there such a thing? Are we there yet? If not, is it something feasible?
Yes and no. As my physics professor used to say, all models are wrong, our goal is to make the least wrong model. It’s literally impossible to simulate every event. For example, what if there was an anthill under the area where the rock is dropped? Maybe that will affect the resulting sound? Maybe, but it’s not going to make a difference to the observer.
We know enough physics to simulate a huge number of simultaneous events, but at some point a model becomes far too complicated (e.g. taking a week to run on a powerful computer), when a more simplified model will do the trick just fine. I personally compare it to FLAC and MP3–FLAC is of course best quality, but will eat up a ton of storage space, and MP3 (with compression) is good enough
What you can do, and is probably the best way to get this to work, is tackle this with machine learning. You will need lots sound samples of rocks, with details of the rock, and feed them to some (probably deep learning) model.
Speech mimicking with AI has shown we are able to mimick voices, so I think a similar approach would work for rocks. Probably need some tuning and a bit different architecture for nice results since the application differs a bit.
It will of course be an approximation, but that is any calculation. Since all models are wrong, but some are less wrong than others.
A big part of the physicist job is to know which factor aren’t relevant in a simulation. You may have heard about approximation like sin(\theta) = \theta or let’s assume a Gaussian distribution
it’s relatively easy to compute the noise of a flat surface falling over a flat surface at a given speed. However, the more factor you add, the more complex is the problem. A good thing is that for a simple phenomenon like that, you’d get something close from real-work even with some approximations. It’s more complicated for example for more “chaotic process” for example once a dice bounced-roll a few time, small difference in the exact position at the first bounce will lead to a different result, making very hard to do a proper simulation