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20052026
most citedParameter Estimation for Multiscale Diffusions

96 citations · 255 across the 43 of their papers we have counts for

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8 papers · 1 filter

math.OC2026

Control Strategies for Multi-Species Wasserstein Gradient Flows

Dante Kalise, Lucas M. Moschen, Grigorios A. Pavliotis

We stabilize stationary states of coupled multi-species McKean--Vlasov equations by feedback control, extending a spectral approach based on the Wasserstein Hessian and a Riccati e…

math.OC2026

Feedback Control and Local Convexification of Wasserstein Gradient Flows

Dante Kalise, Lucas M. Moschen, Grigorios A. Pavliotis

Building on the feedback stabilization framework that we developed for McKean-Vlasov PDEs, we treat a class of entropy-regularized free energies on the flat…

math.OC2025

Linearization-Based Feedback Stabilization of McKean-Vlasov PDEs

Dante Kalise, Lucas M. Moschen, Grigorios A. Pavliotis

We develop a feedback control framework for stabilizing the McKean-Vlasov PDE on the torus. Our goal is to steer the dynamics toward a prescribed stationary distribution or acceler…

math.OC20251 cited

A Spectral Approach to Optimal Control of the Fokker-Planck Equation

Dante Kalise, Lucas M. Moschen, Grigorios A. Pavliotis +1

In this paper, we present a spectral optimal control framework for Fokker-Planck equations based on the standard ground state transformation that maps the Fokker-Planck operator to…

math.OC2024

Computation and Control of Unstable Steady States for Mean Field Multiagent Systems

Sara Bicego, Dante Kalise, Grigorios A. Pavliotis

We study interacting particle systems driven by noise, modeling phenomena such as opinion dynamics. We are interested in systems that exhibit phase transitions i.e. non-uniqueness…

math.OC2020

On stochastic mirror descent with interacting particles: convergence properties and variance reduction

Anastasia Borovykh, Nikolas Kantas, Panos Parpas +1

An open problem in optimization with noisy information is the computation of an exact minimizer that is independent of the amount of noise. A standard practice in stochastic approx…