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Particles in turbulence frequently encounter extreme accelerations between extended periods of quiescence. The occurrence of extreme events is closely related to the intermittent spatial distribution of intense flow structures such as…

Fluid Dynamics · Physics 2019-08-29 Lukas Bentkamp , Cristian C. Lalescu , Michael Wilczek

An arbitrary Lagrangian--Eulerian finite element method and numerical implementation for curved and deforming lipid membranes is presented here. The membrane surface is endowed with a mesh whose in-plane motion need not depend on the…

Computational Physics · Physics 2026-02-24 Amaresh Sahu

The Lagrange-Flux schemes are Eulerian finite volume schemes that make use of an approximate Riemann solver in Lagrangian description with particular upwind convective fluxes. They have been recently designed as variant formulations of…

Numerical Analysis · Mathematics 2017-03-02 Florian De Vuyst

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

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization…

Computer Vision and Pattern Recognition · Computer Science 2017-11-30 Anders Eriksson , Carl Olsson , Fredrik Kahl , Tat-Jun Chin

In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A mathematically exact…

Fluid Dynamics · Physics 2010-06-17 J. Bakosi

This work aims at introducing model methodology and numerical studies related to a Lagrangian stochastic approach applied to the computation of the wind circulation around mills. We adapt the Lagrangian stochastic downscaling method that we…

Computational Engineering, Finance, and Science · Computer Science 2016-11-15 Mireille Bossy , Jose Espina , Jacques Morice , Cristian Paris , Antoine Rousseau

In this paper, we study the problem of computing the effective diffusivity for particles moving in chaotic flows. Instead of solving a convection-diffusion type cell problem in the Eulerian formulation (arising from homogenization theory…

Numerical Analysis · Mathematics 2020-12-17 Zhongjian Wang , Jack Xin , Zhiwen Zhang

Massively-parallel, distributed-memory algorithms for the Lagrangian particle hydrodynamic method [R. Samulyak, X. Wang, H.-C. Chen, Lagrangian particle method for compressible fluid dynamics, J. Comput. Phys., 362 (2018), 1-19] have been…

Computational Physics · Physics 2022-06-27 Shaohua Yuan , Mario Zepeda Aguilar , Nizar Naitlho , Roman Samulyak

The article shows how to learn models of dynamical systems from data which are governed by an unknown variational PDE. Rather than employing reduction techniques, we learn a discrete field theory governed by a discrete Lagrangian density…

Numerical Analysis · Mathematics 2023-08-03 Christian Offen , Sina Ober-Blöbaum

Mean-field-based Lagrangian framework is developed for the fluid turbulence theory. The space- time vector flow is naturally introduced from the mean velocity, which provides the Lagrangian picture based on the mean field in totally…

Fluid Dynamics · Physics 2017-05-10 Taketo Ariki

Inverse problems governed by partial differential equations (PDEs) play a crucial role in various fields, including computational science, image processing, and engineering. Particularly, Darcy flow equation is a fundamental equation in…

Machine Learning · Statistics 2025-07-24 Hongji Wang , Hongqiao Wang , Jinyong Ying , Qingping Zhou

We introduce a modal representation for Lagrangian trajectories in turbulence, termed Lagrangian Proper Orthogonal Decomposition (LPOD). An ensemble of particle trajectories is used to construct velocity time series, which are normalized…

Fluid Dynamics · Physics 2026-04-27 Ron Shnapp , Stefano Brizzolara

Partial differential equations (PDEs) are central to dynamical systems modeling, particularly in hydrodynamics, where traditional solvers often struggle with nonlinearity and computational cost. Lagrangian neural surrogates such as GNS and…

Machine Learning · Computer Science 2025-12-01 Ethan Ji , Yuanzhou Chen , Arush Ramteke , Fang Sun , Tianrun Yu , Jai Parera , Wei Wang , Yizhou Sun

Partial differential equations (PDEs) are among the most universal and parsimonious descriptions of natural physical laws, capturing a rich variety of phenomenology and multi-scale physics in a compact and symbolic representation. This…

Machine Learning · Computer Science 2023-03-31 Steven L. Brunton , J. Nathan Kutz

Various formulations of smooth-particle hydrodynamics (SPH) have been proposed, intended to resolve certain difficulties in the treatment of fluid mixing instabilities. Most have involved changes to the algorithm which either introduce…

Instrumentation and Methods for Astrophysics · Physics 2018-06-19 Philip F. Hopkins

We present a hybrid particle/grid approach for simulating incompressible fluids on collocated velocity grids. We interchangeably use particle and grid representations of transported quantities to balance efficiency and accuracy. A novel…

In this work, we present a novel family of high order accurate numerical schemes for the solution of hyperbolic partial differential equations (PDEs) which combines several geometrical and physical structure preserving properties. First, we…

Numerical Analysis · Mathematics 2025-08-19 Elena Gaburro

The aim of the present paper is to introduce and to discuss inconsistencies errors that may arise when Eulerian and Lagrangian models are coupled for the simulations of turbulent poly-dispersed two-phase flows. In these hydrid models, two…

Fluid Dynamics · Physics 2011-04-07 Sergio Chibbaro , Jean-Pierre Minier

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges…

Atmospheric and Oceanic Physics · Physics 2025-07-10 Niloofar Asefi , Leonard Lupin-Jimenez , Tianning Wu , Ruoying He , Ashesh Chattopadhyay