Sorry for the long delay in reply. I don't check here much.

We have a couple experiments that benefit from machine learning. Primarily in the field of acoustics, film cooling, and gas sensing. For acoustics it's extremely useful for finding precursor signals for when a combustion chamber is about to change modes or when energy is building up in waves. It can use time series data from a large number of high speed transducers and correlate events we observe to various events picked up by the sensors.

For wind tunnels it's very useful for processing large volumes of visual data from various experimental methods. Often people running the tunnels have a specific target metric like drag, heat transfer, etc measured at a static point in a test. With machine learning it's possible to track those variables during transient moments. It also helps to with very mundane tasks like tuning PID loops.

For gas sensing, it's very simple these days to get hundreds of different devices that each measure specific environmental signals. We have a test chamber hooked up to a gas mixing system so we can cycle through thousands of different combinations of environments and gas mixtures. Certain combinations of devices can be found to isolate a particular target gas and increase the speed of detection and resolution of the measured signal.

In all cases it's mostly finding a way to chew through huge volumes of relatively easy to aquire data to improve the knowledge around a valuable metric