We propose a new approach to model the collective dynamics of a population of particles
evolving with time. As is often the case in challenging scientific applications, notably single …

Computing Wasserstein barycenters (aka optimal transport barycenters) is a fundamental
problem in geometry which has recently attracted considerable attention due to many …

The optimal transport problem has recently developed into a powerful framework for various
applications in estimation and control. Many of the recent advances in the theory and …

Abstract Multimarginal Optimal Transport (MOT) has attracted significant interest due to
applications in machine learning, statistics, and the sciences. However, in most applications …

Monge map refers to the optimal transport map between two probability distributions and
provides a principled approach to transform one distribution to another. In spite of the rapid …

Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple
marginals. Optimal transport has evolved into an important tool in many machine learning …

Abstract Multimarginal Optimal Transport (MOT) is the problem of linear programming over
joint probability distributions with fixed marginals. A key issue in many applications is the …

Due to the emergence of technology, electric motors (EMs), an essential part of electric
vehicles (which basically act as engines), have become a pivotal component in modern …

Distribution comparison plays a central role in many machine learning tasks like data
classification and generative modeling. In this study, we propose a novel metric, called …

We investigate the convergence rate of multi-marginal optimal transport costs that are
regularized with the Boltzmann–Shannon entropy, as the noise parameter costs satisfying …