Another fantastic application that the website doesn't mention is the composition / Koopman operator. In control theory (e.g. autonomous drones, cars, robot arms, etc.), most real-world systems are described by nonlinear dynamics which are very difficult to work with (e.g. safety/stability guarantees, optimizing over forward horizons using NMPC, state estimation, etc.) The Koopman operator however gives a globally relevant linear approximation of non-linear systems. In other words, you can treat a nonlinear system as a linear system with fairly high accuracy. This greatly simplifies control and estimation from a computational perspective. You can also learn these linearizations from data. Steve Brunton has some good materials on Koopman theory [2][3], and there are some great applications to control of systems such as soft robots [4].
[1]: https://arxiv.org/abs/1904.02539
[2]: https://youtube.com/playlist?list=PLMrJAkhIeNNSVXUvppZTYNHKQ...