Abstract The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the
Kalman filter (KF) for extremely high-dimensional, possibly nonlinear, and non-Gaussian …

With the ever increasing penetration of renewable energy, smart loads, energy storage, and
new market behavior, today's power grid becomes more dynamic and stochastic, which may …

Ensemble-based history-matching methods have received much attention in reservoir
engineering. In real applications, small ensembles are often used in reservoir simulations to …

In this work, we propose an ensemble 4D-seismic history-matching framework for reservoir
characterization. Compared with similar existing frameworks in the reservoir-engineering …

For operational water management in lowlands and polders (for instance, in the
Netherlands), lowland hydrological models are used for flow prediction, often as an input for …

Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF in: Monthly Weather
Review Volume 143 Issue 7 (2015) Jump to Content Logo Logo Logo Logo Logo Logo …

Characterizing and forecasting the state of the ocean is essential for various scientific,
management, commercial, and recreational applications. This is, however, a challenging …

A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The
optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost …

Ensemble‐based methods are among the state‐of‐the‐art history‐matching algorithms.
However, in practice, they often suffer from ensemble collapse, a phenomenon that …

The time and measurement update for the discrete time Kalman filter can be formulated in
terms of conditional means and covariances. The unscented Kalman filter can be interpreted …