https://hal.inria.fr/hal-01636082v2Bion-Nadal, JocelyneJocelyneBion-NadalCMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche ScientifiqueTalay, DenisDenisTalayTOSCA - TO Simulate and CAlibrate stochastic models - CRISAM - Inria Sophia Antipolis - Méditerranée - Inria - Institut National de Recherche en Informatique et en Automatique - IECL - Institut Élie Cartan de Lorraine - UL - Université de Lorraine - CNRS - Centre National de la Recherche ScientifiqueOn a Wasserstein-type distance between solutions to stochastic differential equationsHAL CCSD2018and phrases: Stochastic differential equationsWasserstein distanceStochastic control[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Talay, Denis2018-06-05 11:05:212022-06-25 19:41:012018-06-05 12:50:02enPreprints, Working Papers, ...https://hal.inria.fr/hal-01636082v1application/pdf2In this paper we introduce a Wasserstein-type distance on the set of the probability distributions of strong solutions to stochastic differential equations. This new distance is defined by restricting the set of possible coupling measures. We prove that it may also be defined by means of the value function of a stochastic control problem whose Hamilton–Jacobi– Bellman equation has a smooth solution, which allows one to deduce a priori estimates or to obtain numerical evaluations. We exhibit an optimal coupling measure and characterizes it as a weak solution to an explicit stochastic differential equation, and we finally describe procedures to approximate this optimal coupling measure. A notable application concerns the following modeling issue: given an exact diffusion model, how to select a simplified diffusion model within a class of admissible models under the constraint that the probability distribution of the exact model is preserved as much as possible?