Maximum Entropy Snapshot Sampling
My name is Marcus Bannenberg and I’m a third year PhD student in the ROMSOC European Industrial Doctorate program which is part of the Marie-Sklodowska-Curie Actions (MSCA). In this second blog I would like to put one
of the model order reduction techniques I use in the spotlight.
The Maximum Entropy Snapshot Sampling (MESS) for reduced basis modelling was first developed in 2020 , and then introduced to a circuit simulation setting . This novel technique reduces the snapshot sample by identifying snapshots that encode essential information. Subsequently a reduced basis is obtained with any orthonormalization process on the reduced sample of snapshots.
It has been shown  that, depending on the recurrence properties of a system, any such basis guarantees that the Euclidean reconstruction error of each snap-shot is bounded from above by , while a similar bound holds true for future snapshots, up to a specific time-horizon.
The MESS model order reduction technique has been the main reduction driver in my ongoing research on Reduced Order Multirate schemes. It provides a very robust and easy to implement reduction method. For more information we like to refer the reader to [1, 3].
 M.W.F.M. Bannenberg, F. Kasolis, M. Günther, and M. Clemens. Maximum entropy snapshot sampling for reduced basis modelling. preprint, 2020.
 H. Broer and F. Takens. Dynamical systems and chaos. Springer-Verlag, New York, 2011.
 F. Kasolis and M. Clemens. Maximum entropy snapshot sampling for reduced basis generation. arXiv preprint arXiv:2005.01280, 2020.