In this survey, we describe controlled interacting particle systems (CIPS) to approximate the
solution of the optimal filtering and the optimal control problems. Part I of the survey is …

In this chapter the state-of-the-art in data assimilation for high-dimensional highly nonlinear
systems is reviewed, and recent developments are highlighted. This knowledge is available …

Solving inverse problems without the use of derivatives or adjoints of the forward model is
highly desirable in many applications arising in science and engineering. In this paper we …

Ensemble Kalman methods are widely used for state estimation in the geophysical sciences.
Their success stems from the fact that they take an underlying (possibly noisy) dynamical …

We investigate the low-dimensional structure of deterministic transformations between
random variables, ie, transport maps between probability measures. In the context of …

We consider filtering in high-dimensional non-Gaussian state-space models with intractable
transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in …

We propose a novel method for sampling and optimization tasks based on a stochastic
interacting particle system. We explain how this method can be used for the following two …

Data assimilation addresses the general problem of how to combine model-based
predictions with partial and noisy observations of the process in an optimal manner. This …

Wi-Fi fingerprint-based indoor positioning systems have extensive application prospects.
Indoor positioning based on channel state information (CSI) becomes a research hotspot …

This paper presents the multivariable extension of the feedback particle filter (FPF) algorithm
for the nonlinear filtering problem in continuous-time. The FPF is a control-oriented …