Noisy nonlocal aggregation model with gradient flow structures

Interacting particle systems provide a fundamental framework for modeling collective behavior in biological, social, and physical systems. In many applications, stochastic perturbations are essential for capturing environmental variability and individual uncertainty, yet their impact on long-term dynamics and equilibrium structure remains incompletely understood, particularly in the presence of nonlocal interactions. We investigate a stochastic interacting particle system governed by potential-driven interactions and its continuum density formulation in the large-population limit. We introduce an energy functional and show that the macroscopic density evolution has a gradient-flow structure in the Wasserstein-2 space. The associated variational framework yields equilibrium states through constrained energy minimization and illustrates how noise regulates the density and mitigates singular concentration. We demonstrate the connection between microscopic and macroscopic descriptions through numerical examples in one and two dimensions. Within the variational framework, we compute energy minimizers and perform a linear stability analysis. The numerical results show that the stable minimizers agree with the long-time dynamics of the macroscopic density model.

💡 Research Summary

The paper investigates a stochastic interacting particle system with non‑local pairwise forces and an external potential, and derives its macroscopic description in the mean‑field limit. Starting from the microscopic dynamics given by a system of stochastic differential equations (SDEs) for N particles, the authors employ propagation‑of‑chaos arguments to show that, as N→∞, the empirical measure converges to a probability density ρ(x,t) that satisfies a non‑local aggregation‑diffusion partial differential equation (PDE):

∂ₜρ = ∇·