arXiv subjects

Angeliki Koutsimpela

Publications and source records attributed to Angeliki Koutsimpela.

5 recordsLinked to original sources

arXiv2026

Mean-field limits for stochastic particle systems on dense graphs

We study stochastic interacting particle systems whose interaction structure is described by dense weighted directed graphs converging to a graphon. In the thermodynamic limit, we prove a law of large numbers for the empirical measure process and derive a deterministic nonlinear master equation describing the macroscopic evolution. The limiting equation retains the heterogeneous interaction structure of the microscopic system through the limiting graphon, allowing for spatially non-homogeneous behaviors such as localized or community-type interactions.

math.PR↗

arXiv2025

Mean-field limits in interacting particle systems with symmetric superlinear rates

We derive the exchange-driven growth (EDG) equations as the mean-field limit of interacting particle systems on the complete graph. Extending previous work, we consider symmetric exchange kernels $c(k, l) = c(l, k)$ satisfying super-linear bounds of the form $c(k, l) \leq C(k^μl^ν + k^νl^μ)$, with $0 \leq μ, ν\leq 2$ and $μ+ ν\leq 3$. Under these conditions the EDG equations are known to have global solutions. We establish a law of large numbers for the empirical measures, showing that the solutions of the EDG equation describe the limiting distribution of cluster sizes in the particle system. Furthermore, we analyse the dynamics of tagged particles and prove convergence to a time-inhomogeneous Markov process governed by a nonlinear master equation, derived via a law of large numbers for size-biased empirical processes.

math.PR↗

arXiv2024

Tagged particles and size-biased dynamics in mean-field interacting particle systems

We establish a connection between tagged particles and size-biased empirical processes in interacting particle systems, in analogy to classical results on the propagation of chaos. In a mean-field scaling limit, the evolution of the occupation number on the tagged particle site converges to a time-inhomogeneous Markov process with non-linear master equation given by the law of large numbers of size-biased empirical measures. The latter are important in recent efforts to understand the dynamics of condensation in interacting particle systems.

math.PR↗

arXiv2024

Optimal control, viscosity approximation and Arrhenius Law for the shallow lake problem

We prove existence of optimal control for the deterministic and stochastic shallow lake problem without any restrictions on the parameter space and we establish a generalization of the Arrhenius Law in the case of noise-dependent potentials, which naturally arise in control theory problems. We also prove a result about convergence of the derivatives in the viscosity approximation of the value function and use this result to derive the Arrhenius Law for the shallow lake problem.

math.OC↗

arXiv2023

On the optimally controlled stochastic shallow lake

We consider the stochastic control problem of the shallow lake and continue the work of G. T. Kossioris, Loulakis, and Souganidis (2019) in three directions. First, we generalise the characterisation of the value function as the viscosity solution of a well-posed problem to include more general recycling rates. Then, we prove approximate optimality under bounded controls and we establish quantitative estimates. Finally, we implement a convergent and stable numerical scheme for the computation of the value function to investigate properties of the optimally controlled stochastic shallow lake. This approach permits to derive tail asymptotics for the invariant distribution and to extend results of Grass, Kiseleva, and Wagener (2015) beyond the small noise limit.

math.OC↗