The notion of propagation of chaos for large systems of interacting particles originates in
statistical physics and has recently become a central notion in many areas of applied …

This paper presents a review and critical analysis on the modeling of the dynamics of
vehicular traffic, human crowds and swarms seen as living and, hence, complex systems. It …

Solving inverse problems without the use of derivatives or adjoints of the forward model is
highly desirable in many applications arising in science and engineering. In this paper we …

We improve recently introduced consensus-based optimization method, proposed in [R.
Pinnau, C. Totzeck, O. Tse, S. Martin, Math. Models Methods Appl. Sci. 27 (2017) 183–204] …

Ensemble Kalman methods are widely used for state estimation in the geophysical sciences.
Their success stems from the fact that they take an underlying (possibly noisy) dynamical …

We introduce a novel first-order stochastic swarm intelligence (SI) model in the spirit of
consensus formation models, namely a consensus-based optimization (CBO) algorithm …

Abstract We study the McKean–Vlasov equation ∂ _t ϱ= β^-1 Δ ϱ+ κ\, ∇ ⋅\,(ϱ ∇ (W ⋆
ϱ)),∂ t ϱ= β-1 Δ ϱ+ κ∇·(ϱ∇(W⋆ ϱ)), with periodic boundary conditions on the torus. We …

Federated learning is an important framework in modern machine learning that seeks to
integrate the training of learning models from multiple users, each user having their own …

In this paper we provide a rigorous convergence analysis for the renowned particle swarm
optimization method by using tools from stochastic calculus and the analysis of partial …

We consider Bayesian inference for large-scale inverse problems, where computational
challenges arise from the need for repeated evaluations of an expensive forward model …