The ensemble Gaussian mixture filter (EnGMF) is a powerful filter for hi...
Data-driven reduced order modeling of chaotic dynamics can result in sys...
The ensemble Gaussian mixture filter combines the simplicity and power o...
Traditional data assimilation uses information obtained from the propaga...
Human movements in urban areas are essential for understanding the
human...
A rapidly growing area of research is the use of machine learning approa...
A fundamental problem of science is designing optimal control policies t...
Particle flow filters that smoothly transform particles from being sampl...
The data assimilation procedures used in many operational numerical weat...
Rejuvenation in particle filters is necessary to prevent the collapse of...
Reduced order modeling (ROM) is a field of techniques that approximates
...
The multifidelity ensemble Kalman filter aims to combine a full-order mo...
Simulation of complex dynamical systems arising in many applications is
...
This work develops a new multifidelity ensemble Kalman filter (MFEnKF)
a...
This paper uses a probabilistic approach to analyze the converge of an
e...
Operational Ensemble Kalman Filtering (EnKF) methods rely on model-speci...
ODE Test Problems (OTP) is an object-oriented MATLAB package offering a ...
Ever since its inception, the Ensemble Kalman Filter has elicited many
h...