Wasserstein gradient flows provide a powerful means of understanding and solving many
diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of …

Nonlinear stochastic dynamical systems are commonly used to model physical processes.
For linear and Gaussian systems, the Kalman filter is optimal in minimum mean squared …

The performance of modern control methods, such as model predictive control, depends
significantly on the accuracy of the system model. In practice, however, stochastic …

A nonlinear filter is developed by representing the state probability density function by a
finite sum of Gaussian density kernels whose mean and covariance are propagated from …

The main objective of this paper is to present the development of the computational
methodology, based on the Gaussian mixture model, that enables accurate propagation of …

We address a decentralized control problem for a multi-agent network whose goal is to track
a moving multi-target system over a cluttered environment. Our problem is comprised of two …

We present a novel approximate inference method for diffusion processes, based on the
Wasserstein gradient flow formulation of the diffusion. In this formulation, the time-dependent …

Several on-line identification approaches have been proposed to identify parameters and
evolution models of engineering systems and structures when sequential datasets are …

Multitarget tracking (MTT) refers to estimating the state and the number of an unknown
number of targets using scans of noisy measurements from one or more sensors in the …

Multi-robot systems must have the ability to accurately estimate relative states between
robots in order to perform collaborative tasks, possibly with no external aiding. Three …