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Building on the framework and algorithm established in the previous talk, we will discuss how to achieve a pointwise convergent DMD algorithm using Koopman generators (or Liouville operators). This will leverage a tool that embeds trajectory information into a function within a reproducing kernel Hilbert spaced called an occupation kernel. This talk will compactify the Koopman generator in two different ways, and will also introduce a new dynamic operator called Liouville weighted Composition Operators. These pointwise convergent results exemplify the advantages gained by taking this perspective over reproducing kernel ilbert spaces. Host: Humberto C Godinez (CCS-2) and Nishant Panda (CCS-3) |