In this work, a stochastic representation based on a physical transport principle is proposed
to account for mesoscale eddy effects on the large‐scale oceanic circulation. This stochastic …

The past few years have witnessed a renewed blossoming of data-driven turbulence
models. Quantification of the concomitant modeling uncertainty, however, has mostly been …

Control of complex turbulent dynamical systems involving strong nonlinearity and high
degrees of internal instability is an important topic in practice. Different from traditional …

We introduce a physically relevant stochastic representation of the rotating shallow water
equations. The derivation relies mainly on a stochastic transport principle and on a …

We investigate the application of a stochastic dynamical model in ensemble Kalman filter
methods. Ensemble Kalman filters are very popular in data assimilation because of their …

This paper proposes an uncertainty quantification method that combines compressed
sensing and POD-Kriging that inherits the benefits of each key element. The compressed …

A stochastic representation based on a physical transport principle is proposed to account
for mesoscale eddy effects on the evolution of the large-scale flow. This framework arises …

In this paper we analyze the theoretical properties of a stochastic representation of the
incompressible Navier–Stokes equations defined in the framework of the modeling under …

Motivated by the concept of “location uncertainty”, initially introduced in, a scheme is sought
to perturb the “location” of a state variable at every forecast time step. Further considering …

In this paper, we present a new method to quantify the uncertainty introduced by the drastic
dimensionality reduction commonly practiced in the field of computational fluid dynamics …