Neural Koopman prior for data assimilation

arXiv:2309.05317v2 Announce Type: replace
Abstract: With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical priors on the data and from mathematical knowledge. In this paper, we use a neural network architecture which leverages the …

abstract architectures arxiv availability box computational cs.lg data data-driven datasets differentiation network networks neural network neural networks observation power prior scale tools type