1 citations · 1 across the 13 of their papers we have counts for
18 papers
A modified particle filter that reduces weight collapse
Shay Gilpin, Michael Herty
Particle filters are a widely used Monte Carlo based data assimilation technique that estimates the probability distribution of a system's state conditioned on observations through…
Control of Conservation Laws in the Nonlocal-to-Local Limit
Jan Friedrich, Michael Herty, Claudia Nocita
We analyze a class of control problems where the initial datum acts as a control and the state is given by the entropy solution of (local) conservation laws by a nonlocal-to-local…
Sparse control in microscopic and mean-field leader-follower models
Melanie Harms, Michael Herty, Chiara Segala +1
This work investigates the decay properties of Lyapunov functions in leader-follower systems seen as a sparse control framework. Starting with a microscopic representation, we esta…
Boundary Stabilization with restricted observability
Mapundi Kondwani Banda, Jan Friedrich, Michael Herty
Lyapunov functions are popularly used to investigate the stabilization problem of systems of hyperbolic conservation laws with boundary controls. In real life applications often no…
Micro-Macro Decomposition of Particle Swarm Optimization Methods
Michael Herty, Sara Veneruso
Solving non-convex minimization problems using multi-particle metaheuristic derivative-free optimization methods is still an active area of research. Popular methods are Particle S…
Fast and robust consensus-based optimization via optimal feedback control
Yuyang Huang, Michael Herty, Dante Kalise +1
We propose a variant of consensus-based optimization (CBO) algorithms, controlled-CBO, which introduces a feedback control term to improve convergence towards global minimizers of…
Kinetic models for optimization: a unified mathematical framework for metaheuristics
Giacomo Borghi, Michael Herty, Lorenzo Pareschi
Metaheuristic algorithms, widely used for solving complex non-convex and non-differentiable optimization problems, often lack a solid mathematical foundation. In this review, we ex…
Dynamics of measure-valued agents in the space of probabilities
Giacomo Borghi, Michael Herty, Andrey Stavitskiy
Motivated by the development of dynamics in probability spaces, we propose a novel multi-agent dynamic of consensus type where each agent is a probability measure. The agents move…
Exponential turnpike property for particle systems and mean-field limit
Michael Herty, Yizhou Zhou
This work is concerned with the exponential turnpike property for optimal control problems of particle systems and their mean-field limit. Under the assumption of the strict dissip…
Kinetic Theories for Metropolis Monte Carlo Methods
Michael Herty, Christian Ringhofer
We consider generalizations of the classical inverse problem to Bayesien type estimators, where the result is not one optimal parameter but an optimal probability distribution in p…
Source Identification by Consensus-Based Optimization
Jan Friedrich, Sarah Schraven, Fabian Kiessling +1
A consensus-based optimization (CBO) algorithm, which enables derivative and mesh-free optimization, is presented to localize a bioluminescent source. The light propagation is mode…
The turnpike property for high-dimensional interacting agent systems in discrete time
Martin Gugat, Michael Herty, Jiehong Liu +1
We investigate the interior turnpike phenomenon for discrete-time multi-agent optimal control problems. While for continuous systems the turnpike property has been established, we…
Controllability of continuous networks and a kernel-based learning approximation
Michael Herty, Chiara Segala, Giuseppe Visconti
Residual deep neural networks are formulated as interacting particle systems leading to a description through neural differential equations, and, in the case of large input data, t…
Data/moment-driven approaches for fast predictive control of collective dynamics
Giacomo Albi, Sara Bicego, Michael Herty +3
Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics ha…
Model predictive control strategies using consensus-based optimization
Giacomo Borghi, Michael Herty
Model predictive control strategies require to solve in an sequential manner, many, possibly non-convex, optimization problems. In this work, we propose an interacting stochastic a…
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Christian Fiedler, Michael Herty, Sebastian Trimpe
Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems. While the continu…
On kernel-based statistical learning in the mean field limit
Christian Fiedler, Michael Herty, Sebastian Trimpe
In many applications of machine learning, a large number of variables are considered. Motivated by machine learning of interacting particle systems, we consider the situation when…
Constrained consensus-based optimization
Giacomo Borghi, Michael Herty, Lorenzo Pareschi
In this work we are interested in the construction of numerical methods for high dimensional constrained nonlinear optimization problems by particle-based gradient-free techniques.…