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 …

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 …

This paper provides a unifying mean field based framework for the derivation and analysis of
ensemble Kalman methods. Both state estimation and parameter estimation problems are …

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 …

Nonlinear filtering is used in online estimation of a dynamic hidden variable from incoming
data and has vast applications in different fields, ranging from engineering, machine …

Various particle filters have been proposed over the last couple of decades with the common
feature that the update step is governed by a type of control law. This feature makes them an …

This book enables readers to understand, model, and predict complex dynamical systems
using new methods with stochastic tools. The author presents a unique combination of …

We investigate the application of ensemble transform approaches to Bayesian inference of
logistic regression problems. Our approach relies on appropriate extensions of the popular …

Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the
feedback particle filter (FPF) are numerical algorithms to approximate the solution of the …

Surge active control can expand the stable operating range of the compressor. However, the
difficulty of flow measurement, dynamic uncertainty disturbance, actuator delay …