We develop a weak-form sparse identification method for interacting particle systems (IPS)
with the primary goals of reducing computational complexity for large particle number N and …

We establish the local asymptotic normality (LAN) property for estimating a multidimensional
parameter in the drift of a system of N interacting particles observed over a fixed time horizon …

In this paper, we consider the problem of joint parameter estimation for drift and diffusion
coefficients of a stochastic McKean–Vlasov equation and for the associated system of …

We study SDE and PDE models for opinion dynamics under bounded confidence, for a
range of different boundary conditions, with and without the inclusion of a radical population …

Computational models have become a powerful tool in the quantitative sciences to
understand the behavior of complex systems that evolve in time. However, they often contain …

We consider a stochastic system of NN interacting particles with constant diffusion coefficient
and drift linear in space, time‐depending on two unknown deterministic functions. Our …

This paper concerns the nonparametric estimation problem of the distribution-state
dependent drift vector field in an interacting $ N $-particle system. Observing single …

We study the problem of parameter estimation for large exchangeable interacting particle
systems when a sample of discrete observations from a single particle is known. We …

This paper proves that, under a monotonicity condition, the invariant probability measure of
a McKean–Vlasov process can be approximated by weighted empirical measures of some …

We study the problem of learning unknown parameters in stochastic interacting particle
systems with polynomial drift, interaction, and diffusion functions from the path of one single …