The occupation kernel method
October 15, 2024 — October 15, 2024
Kernel tricks for trajectories. That is to say, another 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.
1 Incoming
The Incredible Occupation Kernel! // A peek at my research (video)
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We capture governing principles of dynamical systems with operators.
There is a host of natural phenomena that is impossible to describe from first principles. We design algorithms that use captured snapshots of occurrences, and we use concepts from operator and Hilbert space theory to uncover the underlying governing principles of our world.
Reproducing Kernels and Functionals (Theory of Machine Learning) (video)