The occupation kernel method

Kernel tricks for trajectories. That is to say, the other kernel trick for trajectories.

I am told e.g. that this generalises the Radon transform, as seen in tomography, so I guess I should know about that for my own work.

Applications include the identification of forcing fields for functions by sparsely observable trajectories, without finite-difference approximations, for system identification and functional inverse problems.