fadaforhighdimensional

In this paper, a novel data assimilation approach which only requires a small number of samples and can be applied to high-dimensional systems. This approach is based on linear latent variable models and leverages machinery to achieve fast implementation. It does not require computing the high-dimensional sample covariance matrix, which provides significant computational speed-up. Since it is performed without calculating likelihood function, it can be applied to data sssimilation problems in which likelihood is intractable.

In fact, in many cases, the distribution of observation noise is unknown, and the observation model is viewed as a black box which outputs observation data given the input states.

Methodology

Linear Latent Variable Model (LLVM)