This work develops a new multifidelity ensemble Kalman filter (MFEnKF) algorithm based on
a linear control variate framework. The approach allows for rigorous multifidelity extensions …

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of
particles into samples from the posterior distribution. Particles move in state space under the …

This work considers multirate generalized-structure additively partitioned Runge–Kutta
methods for solving stiff systems of ordinary differential equations with multiple time scales …

Ever since its inception, the ensemble Kalman filter (EnKF) has elicited many heuristic
approaches that sought to improve it. One such method is covariance localization, which …

Exponential integrators are special time discretization methods where the traditional linear
system solves used by implicit schemes are replaced with computing the action of matrix …

Data assimilation is a Bayesian inference process that obtains an enhanced understanding
of a physical system of interest by fusing information from an inexact physics-based model …

Reduced order modeling (ROM) is a field of techniques that approximates complex physics-
based models of real-world processes by inexpensive surrogates that capture important …

Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the
number of particles is insufficient to properly sample the high-probability regions of the state …

Traditional data assimilation uses information obtained from the propagation of one physics-
driven model and combines it with information derived from real-world observations in order …

Time integration methods for solving initial value problems are an important component of
many scientific and engineering simulations. Implicit time integrators are desirable for their …