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Scale-resolving simulations of high Reynolds number incompressible flows are often limited by the Courant-Friedrichs-Lewy (CFL) stability restriction imposed by explicit time-stepping schemes, resulting in small time step sizes and long…

Fluid Dynamics · Physics 2026-04-20 Henrik Wüstenberg , Alexandra Liosi , Spencer J. Sherwin , Joaquim Peiró , David Moxey

As one of the major challenges for the conservative smoothed particle hydrodynamics (SPH) method, the zero-order consistency issue, although thought to be mitigated by the particle regularization scheme, such as the transport velocity…

Fluid Dynamics · Physics 2025-07-25 Feng Wang , Xiangyu Hu

A numerical method based on smoothed particle hydrodynamics with adaptive spatial resolution (SPH-ASR) was developed for simulating free surface flows. This method can reduce the computational demands while maintaining the numerical…

Computational Physics · Physics 2021-07-28 Xiufeng Yang , Song-Charng Kong , Moubin Liu , Qingquan Liu

When the effects of relative motion at the solid object interfaces are not negligible, the contact method is required in the smoothed particle hydrodynamics (SPH) method to prevent virtual shear and tensile stresses. However, there is still…

Fluid Dynamics · Physics 2024-09-06 Wenbin Liu , Zhuoping Duan , Yan Liu , Fenglei Huang

Particle shifting techniques (PST) have been used to improve the accuracy of the Smoothed Particle Hydrodynamics (SPH) method. Shifting ensures that the particles are distributed homogeneously in space. This may be performed by moving the…

Fluid Dynamics · Physics 2022-03-15 Dinesh Adepu , Prabhu Ramachandran

A Quantified Model-Competition (QMC) mechanism for multi-scale flows is extracted from the integral (analytical) solution of the Boltzmann-BGK model equation. In the QMC mechanism, the weight of the rarefied model and the weight of the…

Computational Physics · Physics 2020-07-15 Sha Liu , Chengwen Zhong , Ming Fang

In the realm of statistical learning, the increasing volume of accessible data and increasing model complexity necessitate robust methodologies. This paper explores two branches of robust Bayesian methods in response to this trend. The…

Methodology · Statistics 2024-12-02 Masahiro Tanaka

The error of smoothed particle hydrodynamics (SPH) using kernel for particle-based approximation mainly comes from smoothing and integration errors. The choice of kernels has a significant impact on the numerical accuracy, stability and…

Computational Physics · Physics 2023-09-06 Zhentong Wang , Bo Zhang , Oskar J. Haidn , Xiangyu Hu

The Weakly-Compressible Smoothed Particle Hydrodynamics (WCSPH) method is a Lagrangian method that is typically used for the simulation of incompressible fluids. While developing an SPH-based scheme or solver, researchers often verify their…

Fluid Dynamics · Physics 2022-01-14 Pawan Negi , Prabhu Ramachandran

In this work we discuss an approximate model for the propagation of deep irrotational water waves, specifically the model obtained by keeping only quadratic nonlinearities in the water waves system under the Zakharov/Craig-Sulem…

Analysis of PDEs · Mathematics 2025-01-06 Vincent Duchêne , Benjamin Melinand

In this paper, we present an efficient numerical method to address a thermodynamically consistent gas flow model in porous media involving compressible gas and deformable rock. The accurate modeling of gas flow in porous media often poses…

Numerical Analysis · Mathematics 2026-02-16 Huangxin Chen , Yuxiang Chen , Jisheng Kou , Shuyu Sun

A PCA-based, machine learning version of the SPH method is proposed. In the present scheme, the smoothing tensor is computed to have their eigenvalues proportional to the covariance's principal components, using a modified octree data…

Computational Physics · Physics 2021-06-17 Eraldo Pereira Marinho

The aim of this paper is to investigate the unstable nature of pressure computation focusing on incompressible flow modeling through the projection-based particle methods. A new approach from the original viewpoint of the momentum…

Fluid Dynamics · Physics 2022-03-18 Liang-Yee Cheng , Rubens Augusto Amaro Junior , Eric Henrique Favero

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algorithm simulates samples…

Machine Learning · Statistics 2018-07-11 Changyou Chen , Ruiyi Zhang , Wenlin Wang , Bai Li , Liqun Chen

We consider the minimization of a function defined on a Riemannian manifold $\mathcal{M}$ accessible only through unbiased estimates of its gradients. We develop a geometric framework to transform a sequence of slowly converging iterates…

Machine Learning · Computer Science 2018-06-11 Nilesh Tripuraneni , Nicolas Flammarion , Francis Bach , Michael I. Jordan

This paper introduces a novel methodology for modeling stationary shock waves in porous materials, which employs the recently developed moving window technique. The core of this method is the iterative adjustment of the reference frame to…

Computational Physics · Physics 2025-07-22 G. D. Rublev , S. A. Murzov

While 4D Gaussian Splatting (4DGS) has revolutionized high-fidelity dynamic reconstruction, safeguarding the intellectual property of these assets remains an open challenge. Conventional steganographic techniques often neglect the…

Computer Vision and Pattern Recognition · Computer Science 2026-05-22 Sifan Zhou , Hang Zhang , Yuhang Wang , Ming Li

The Trefftz Discontinuous Galerkin (TDG) method is a technique for approximating the Helmholtz equation (or other linear wave equations) using piecewise defined local solutions of the equation to approximate the global solution. When…

Numerical Analysis · Mathematics 2019-08-16 Lise-Marie Imbert-Gerard , Peter Monk

In this study, we propose a graph neural network (GNN) model for efficiently predicting the flow behavior of non-Newtonian fluids with free surface dynamics. The numerical analysis of non-Newtonian fluids presents significant challenges, as…

Fluid Dynamics · Physics 2025-09-30 Hyo-Jin Kim , Jaekwang Kim , Hyung-Jun Park

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving…

Computational Physics · Physics 2017-01-04 Huan Lei , Xiu Yang , Zhen Li , George Karniadakis
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