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Related papers: Bayesian Methods for the Navier-Stokes Equations

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The present paper deals with the numerical solution of the incompressible Navier-Stokes equations using high-order discontinuous Galerkin (DG) methods for discretization in space. For DG methods applied to the dual splitting projection…

Numerical Analysis · Mathematics 2017-10-25 Niklas Fehn , Wolfgang A. Wall , Martin Kronbichler

Incompressible flow solvers based on strong-form meshfree methods represent arbitrary geometries without the need for a global mesh system. However, their local evaluations make it difficult to satisfy incompressibility at the discrete…

Numerical Analysis · Mathematics 2026-05-05 Takeharu Matsuda , Satoshi Ii

We develop a variational multiscale proper orthogonal decomposition reduced-order model for turbulent incompressible Navier-Stokes equations. The error analysis of the full discretization of the model is presented. All error contributions…

Numerical Analysis · Mathematics 2013-06-03 Traian Iliescu , Zhu Wang

We investigate the long-time behavior of solutions to a stochastically forced one-dimensional Navier-Stokes system, describing the motion of a compressible viscous fluid, in the case of linear pressure law. We prove existence of an…

Analysis of PDEs · Mathematics 2018-02-13 Michele Coti Zelati , Nathan Glatt-Holtz , Konstantina Trivisa

A new probabilistic representation is presented for solutions of the incompressible Navier-Stokes equations in 3 dimensions with given forcing and initial velocity. This representation expresses solutions as scaled conditional expectations…

Probability · Mathematics 2007-05-23 M. Ossiander

This paper presents the variational discretization of the compressible Navier-Stokes-Fourier system, in which the viscosity and the heat conduction terms are handled within the variational approach to nonequilibrium thermodynamics as…

Differential Geometry · Mathematics 2022-02-09 Benjamin Couéraud , François Gay-Balmaz

The Gaussian-filtered Navier-Stokes equations are examined theoretically and a generalized theory of their numerical stability is proposed. Using the exact expansion series of subfilter-scale stresses or integration by parts, the terms…

Fluid Dynamics · Physics 2007-05-23 Masato Ida , Nobuyuki Oshima

Governing equations of motion for a viscous incompressible material surface are derived from the balance laws of continuum mechanics. The surface is treated as a time-dependent smooth orientable manifold of codimension one in an ambient…

Mathematical Physics · Physics 2018-10-10 Thomas Jankuhn , Maxim A. Olshanskii , Arnold Reusken

This work proposes a new stabilized $P_1\times P_0$ finite element method for solving the incompressible Navier--Stokes equations. The numerical scheme is based on a reduced Bernardi--Raugel element with statically condensed face bubbles…

Numerical Analysis · Mathematics 2023-04-06 Yuwen Li , Ludmil Zikatanov

This paper introduces a Variational Multiscale Stabilization (VMS) formulation of the incompressible Navier--Stokes equations that utilizes the Finite Element Exterior Calculus (FEEC) framework. The FEEC framework preserves the geometric…

Numerical Analysis · Mathematics 2025-12-17 Kevin Dijkstra , Deepesh Toshniwal

In the last three decades, Fourier analysis methods have known a growing importance in the study of linear and nonlinear PDE's. In particular, techniques based on Littlewood-Paley decomposition and paradifferential calculus have proved to…

Analysis of PDEs · Mathematics 2015-07-10 Raphaël Danchin

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 location uncertainty (LU). This setup built from a stochastic…

Analysis of PDEs · Mathematics 2023-02-01 Arnaud Debussche , Berenger Hug , Etienne Memin

We provide spatial discretizations of nonlinear incompressible Navier-Stokes equations with inputs and outputs in the form of matrices ready to use in any numerical linear algebra package. We discuss the assembling of the system operators…

Mathematical Software · Computer Science 2017-07-28 Maximilian Behr , Peter Benner , Jan Heiland

A coupled forward-backward stochastic differential system (FBSDS) is formulated in spaces of fields for the incompressible Navier-Stokes equation in the whole space. It is shown to have a unique local solution, and further if either the…

Mathematical Physics · Physics 2014-03-04 Freddy Delbaen , Jinniao Qiu , Shanjian Tang

We consider an unregularized optimal control problem subject to the steady-state Navier-Stokes equations. We derive the existence of optimal solutions and prove first- and second-order optimality conditions. To approximate solutions to the…

Numerical Analysis · Mathematics 2026-05-26 Francisco Fuica , Nicolai Jork

In this paper we derive a representation of the deterministic 3-dimensional Navier-Stokes equations based on stochastic Lagrangian paths. The particle trajectories obey SDEs driven by a uniform Wiener process; the inviscid Weber formula for…

Probability · Mathematics 2010-03-16 Peter Constantin , Gautam Iyer

A numerical method for the two-dimensional, incompressible Navier--Stokes equations in vorticity--streamfunction form is proposed, which employs semi-Lagrangian discretizations for both the advection and diffusion terms, thus achieving…

Numerical Analysis · Mathematics 2018-01-30 Luca Bonaventura , Roberto Ferretti , Lorenzo Rocchi

We construct solutions to the randomly-forced Navier--Stokes--Poisson system in periodic three-dimensional domains or in the whole three-dimensional Euclidean space. These solutions are weak in the sense of PDEs and also weak in the sense…

Analysis of PDEs · Mathematics 2020-05-04 Donatella Donatelli , Pierangelo Marcati , Prince Romeo Mensah

In the first part of this article, we obtain a linear system whose the solution solves the time-independent incompressible Navier-Stokes system for the special case in which the external forces vector is a gradient. In a second step we…

General Mathematics · Mathematics 2019-08-27 Fabio Botelho

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems.…

Computation · Statistics 2018-03-14 Andreas Svensson , Thomas B. Schön , Fredrik Lindsten