We commonly refer to state estimation theory in geosciences as data assimilation (DA). This
term encompasses the entire sequence of operations that, starting from the observations of a …

Weather forecast and earth system models usually have a number of parameters, which are
often optimized manually by trial and error. Several studies have proposed objective …

A novel method, based on the combination of data assimilation and machine learning is
introduced. The new hybrid approach is designed for a two-fold scope:(i) emulating hidden …

Joint estimation of states and time‐varying parameters of linear stochastic systems is of
practical importance for fault diagnosis and fault tolerant control. The known fact is that …

Intensive research in the field of mathematical modeling of hydraulic servo systems has
shown that their mathematical models have many important details which cannot be …

Successful implementation of many control strategies is mainly based on accurate
knowledge of the system and its parameters. Besides the stochastic nature of the systems …

This contribution presents a hierarchical Bayesian filter for recursive input, state and
parameter estimation using spatially incomplete and noisy output-only vibration …

For modelling geophysical systems, large-scale processes are described through a set of
coarse-grained dynamical equations while small-scale processes are represented via …

In this article, an overview of Bayesian methods for sequential simulation from posterior
distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is …

Rendezvous and docking missions have been a mainstay of space exploration from the
Apollo program through present day operations with the International Space Station. There …