The dynamic Schrödinger bridge problem seeks a stochastic process that defines a
transport between two target probability measures, while optimally satisfying the criteria of …

Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

A central research challenge for the mathematical sciences in the twenty-first century is the
development of principled methodologies for the seamless integration of (often vast) data …

With the rapid increase of available data for complex systems, there is great interest in the
extraction of physically relevant information from massive datasets. Recently, a framework …

A comprehensive introduction to the core issues of stochastic differential equations and their
effective application Introduction to Stochastic Differential Equations with Applications to …

The deterioration of engineering systems might reduce the system reliability and prompt
maintenance operations that may disrupt the ability of the systems to provide regular service …

We address data-driven learning of the infinitesimal generator of stochastic diffusion
processes, essential for understanding numerical simulations of natural and physical …

A line of recent work has analyzed the behavior of the Expectation-Maximization (EM)
algorithm in the well-specified setting, in which the population likelihood is locally strongly …

Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still
challenging to obtain accurate results and plausible uncertainty estimates when using these …

We consider N independent stochastic processes (** (t), t∈[0, T]), i= 1,..., N, defined by a
one-dimensional stochastic differential equation, which are continuously observed …