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This paper considers the discretization of the time-dependent Navier-Stokes equations with the family of inf-sup stabilized Scott-Vogelius pairs recently introduced in [John/Li/Merdon/Rui, arXiv:2206.01242, 2022] for the Stokes problem.…

Numerical Analysis · Mathematics 2022-12-22 Naveed Ahmed , Volker John , Xu Li , Christian Merdon

This paper is concerned with moving mesh finite difference solution of partial differential equations. It is known that mesh movement introduces an extra convection term and its numerical treatment has a significant impact on the stability…

Numerical Analysis · Mathematics 2015-07-31 Weizhang Huang

High-fidelity, high-resolution numerical simulations are crucial for studying complex multiscale phenomena in fluid dynamics, such as turbulent flows and ocean waves. However, direct numerical simulations with high-resolution solvers are…

Numerical Analysis · Mathematics 2025-04-14 Wuzhe Xu , Yulong Lu , Lian Shen , Anqing Xuan , Ali Barzegari

In this paper, we consider a fast and second-order implicit difference method for approximation of a class of time-space fractional variable coefficients advection-diffusion equation. To begin with, we construct an implicit difference…

Numerical Analysis · Mathematics 2019-07-12 Yong-Liang Zhao , Ting-Zhu Huang , Xian-Ming Gu , Wei-Hua Luo

Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured data through a denoising process. When equipped with a guidance mechanism, these models can also…

Machine Learning · Computer Science 2026-05-04 Saeed Mohseni-Sehdeh , Walid Saad , Kei Sakaguchi , Tao Yu

In this work, we investigate a system of interacting particles governed by a set of stochastic differential equations. Our main goal is to rigorously demonstrate that the empirical measure associated with the particle system converges…

Probability · Mathematics 2025-08-12 Filippo Giovagnini , Dan Crisan

Inverse problems involve making inference about unknown parameters of a physical process using observational data. This paper investigates an important class of inverse problems -- the estimation of the initial condition of a…

Methodology · Statistics 2023-02-09 Xiao Liu , Kyongmin Yeo

In this work we study different Implicit-Explicit (IMEX) schemes for incompressible flow problems with variable viscosity. Unlike most previous work on IMEX schemes, which focuses on the convective part, we here focus on treating parts of…

Numerical Analysis · Mathematics 2024-08-21 Gabriel R. Barrenechea , Ernesto Castillo , Douglas R. Q. Pacheco

A generalized approach to decompose the compressible Navier-Stokes equations into an equivalent set of coupled equations for flow and acoustics is introduced. As a significant extension to standard hydrodynamic/acoustic splitting methods,…

Fluid Dynamics · Physics 2021-08-18 Roland Ewert , Johannes Kreuzinger

Space fractional convection diffusion equation describes physical phenomena where particles or energy (or other physical quantities) are transferred inside a physical system due to two processes: convection and superdiffusion. In this…

Numerical Analysis · Mathematics 2014-05-20 Minghua Chen , Weihua Deng

Continuing on our previous work [ArXiv:1212.2644], we develop semi-implicit numerical methods for solving low Mach number fluctuating hydrodynamic equations appropriate for modeling diffusive mixing in isothermal mixtures of fluids with…

Numerical Analysis · Mathematics 2016-01-20 A. J. Nonaka , Y. Sun , J. B. Bell , A. Donev

In this work, we aimed to replicate and extend the results presented in the DiffFluid paper[1]. The DiffFluid model showed that diffusion models combined with Transformers are capable of predicting fluid dynamics. It uses a denoising…

Fluid Dynamics · Physics 2025-07-14 Yannick Gachnang , Vismay Churiwala

This paper presents a gradient-based reconstruction approach for simulations of compressible single and multi-species Navier-Stokes equations. The novel feature of the proposed algorithm is the efficient reconstruction via derivative…

Fluid Dynamics · Physics 2022-11-29 Amareshwara Sainadh Chamarthi

The paper presents numerical methods for unsteady flows of a viscous incompressible fluid in internal domains with many inlet/outlet sections. The novel variants of dissipative boundary conditions augmented by the inertia terms are used at…

Computational Physics · Physics 2019-12-10 Jacek Szumbarski

We present a new method for making diffusion models faster to sample. The method distills many-step diffusion models into few-step models by matching conditional expectations of the clean data given noisy data along the sampling trajectory.…

Machine Learning · Computer Science 2024-06-07 Tim Salimans , Thomas Mensink , Jonathan Heek , Emiel Hoogeboom

We propose in this paper efficient first/second-order time-stepping schemes for the evolutional Navier-Stokes-Nernst-Planck-Poisson equations. The proposed schemes are constructed using an auxiliary variable reformulation and sophisticated…

Numerical Analysis · Mathematics 2023-05-17 Xiaolan Zhou , Chuanju Xu

Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps…

Machine Learning · Computer Science 2025-03-26 Kai Wang , Mingjia Shi , Yukun Zhou , Zekai Li , Zhihang Yuan , Yuzhang Shang , Xiaojiang Peng , Hanwang Zhang , Yang You

We present a simple and efficient variational finite difference method for simulating time-dependent Stokes flow in the presence of irregular free surfaces and moving solid boundaries. The method uses an embedded boundary approach on…

Computational Physics · Physics 2011-05-25 Christopher Batty , Robert Bridson

This paper proposes a novel particle scheme that provides convergent approximations of a weak solution of the Navier-Stokes equations for the 1-D flow of a viscous compressible fluid. Moreover, it is shown that all differential inequalities…

Analysis of PDEs · Mathematics 2023-01-12 Iasson Karafyllis , Markos Papageorgiou

Building on recent advances in scientific machine learning and generative modeling for computational fluid dynamics, we propose a conditional score-based diffusion model designed for multi-scenarios fluid flow prediction. Our model…

Machine Learning · Computer Science 2025-06-02 Wilfried Genuist , Éric Savin , Filippo Gatti , Didier Clouteau