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Convergence of particle systems to the Vlasov-Navier-Stokes equations is a difficult topic with only fragmentary results. Under a suitable modification of the classical Stokes drag force interaction, here a partial result in this direction…

Probability · Mathematics 2018-07-31 Franco Flandoli , Marta Leocata , Cristiano Ricci

Exponential stabilizability of the incompressible Navier-Stokes equations under dynamic slip boundary conditions toward arbitrary time-dependent trajectories is proven. The feedback control law is constructed explicitly using oblique…

Analysis of PDEs · Mathematics 2026-02-12 Buddhika Priyasad , Sérgio S. Rodrigues

Our understanding of physical systems generally depends on our ability to match complex computational modelling with measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities,…

Plasma Physics · Physics 2020-01-22 M. F. Kasim , T. P. Galligan , J. Topp-Mugglestone , G. Gregori , S. M. Vinko

We study the inviscid limit problem for the incompressible Navier-Stokes equation on a half-plane with a Navier boundary condition depending on the viscosity. On one hand, we prove the $L^2$ convergence of Leray solutions to the solution of…

Analysis of PDEs · Mathematics 2014-12-11 Matthew Paddick

We consider the flow of an { ideal} fluid in a 2D-bounded domain, admitting flows through the boundary of this domain. The flow is described by Euler equations with \textit{non-homogeneous } Navier slip boundary conditions. These conditions…

Analysis of PDEs · Mathematics 2024-09-25 N. V. Chemetov , S. N. Antontsev

We prove the existence and uniqueness of global, probabilistically strong, analytically strong solutions of the 2D Stochastic Navier-Stokes Equation under Navier boundary conditions. The choice of noise includes a large class of additive,…

Probability · Mathematics 2023-08-17 Daniel Goodair

Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy and incomplete data or inherent non-uniqueness. A prominent example is full waveform inversion, which estimates Earth's subsurface…

Geophysics · Physics 2026-03-03 Ali Siahkoohi , Kamal Aghazade , Ali Gholami

Reynolds-averaged Navier--Stokes (RANS) closure must be sensitive to the flow physics, including nonlocality and anisotropy of the effective eddy viscosity. Recent approaches used forced direct numerical simulations to probe these effects,…

Fluid Dynamics · Physics 2024-09-24 Jessie Liu , Florian Schäfer , Spencer H. Bryngelson , Tamer A. Zaki , Ali Mani

The random forced Navier-Stokes equation can be obtained as a variational problem of a proper action. By virtue of incompressibility, the integration over transverse components of the fields allows to cast the action in the form of a large…

Fluid Dynamics · Physics 2009-11-10 R. Collina , R. Livi , A. Mazzino

The present paper is concerned with an inviscid limit problem of radially symmetric stationary solutions for an exterior problem in $\mathbb{R}^n (n\ge 2)$ to compressible Navier-Stokes equation, describing the motion of viscous barotropic…

Analysis of PDEs · Mathematics 2023-09-01 Itsuko Hashimoto , Akitaka Matsumura

We analyze the steady motion of a viscous incompressible fluid in a three-dimensional channel containing an obstacle through the Navier-Stokes equations with mixed boundary conditions: the inflow is given by a fairly general datum and the…

Analysis of PDEs · Mathematics 2020-08-21 Gianmarco Sperone

In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of PDEs is enforcing physical constraints and boundary conditions. In this work,…

Computational Physics · Physics 2020-02-18 Arvind T. Mohan , Nicholas Lubbers , Daniel Livescu , Michael Chertkov

Many fundamental problems in fluid dynamics are related to the effects of solid boundaries. In general, they install sharp gradients and contribute to the developement of small-scale structures, which are computationally expensive to…

Fluid Dynamics · Physics 2024-12-20 Ciro S. Campolina , Alexei A. Mailybaev

Inverse problems are prevalent in numerous scientific and engineering disciplines, where the objective is to determine unknown parameters within a physical system using indirect measurements or observations. The inherent challenge lies in…

Computational Physics · Physics 2025-02-06 Georgios E. Pavlou , Vasiliki Pavlidou , Vagelis Harmandaris

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen's spatially varying material properties from noisy…

This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples from the joint distribution of inferred variables and measurements are assumed available, while…

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes…

Machine Learning · Computer Science 2023-06-02 Florent Bonnet , Ahmed Jocelyn Mazari , Paola Cinnella , Patrick Gallinari

Mathematical estimates for the Navier-Stokes equations are traditionally expressed in terms of the Grashof number, which is a dimensionless measure of the magnitude of the forcing and hence a control parameter of the system. However,…

Fluid Dynamics · Physics 2025-12-18 Ritwik Mukherjee , John D. Gibbon , Dario Vincenzi

We consider the three-dimensional incompressible Navier-Stokes equations in a bounded domain with Navier boundary conditions. We provide a sufficient condition for the absence of anomalous energy dissipation without making assumptions on…

Analysis of PDEs · Mathematics 2026-03-20 Claude Bardos , Daniel W. Boutros , Edriss S. Titi

Bayesian inference for inverse problems involves computing expectations under posterior distributions -- e.g., posterior means, variances, or predictive quantities -- typically via Monte Carlo (MC) estimation. When the quantity of interest…

Machine Learning · Statistics 2026-02-26 Ali Siahkoohi , Hyunwoo Oh
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