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相关论文: Boosting the accuracy of SPH techniques: Newtonian…

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The numerical convergence of smoothed particle hydrodynamics (SPH) can be severely restricted by random force errors induced by particle disorder, especially in shear flows, which are ubiquitous in astrophysics. The increase in the number…

天体物理仪器与方法 · 物理学 2015-06-04 Walter Dehnen , Hossam Aly

We present a set of new smoothing kernels for smoothed particle hydrodynamics (SPH) that improve the convergence of the method without any additional computational cost. These kernels are generated through a linear combination of other SPH…

天体物理仪器与方法 · 物理学 2026-02-04 Robert Wissing , Thomas R. Quinn , Ben Keller , James Wadsley , Sijing Shen

The problem of consistency of smoothed particle hydrodynamics (SPH) has demanded considerable attention in the past few years due to the ever increasing number of applications of the method in many areas of science and engineering. A loss…

In smoothed particle hydrodynamics (SPH) method, the particle-based approximations are implemented via kernel functions, and the evaluation of performance involves two key criteria: numerical accuracy and computational efficiency. In the…

计算工程、金融与科学 · 计算机科学 2024-05-09 Zhentong Wang , Chi Zhang , Oskar J. Haidn , Xiangyu Hu

The smoothed-particle hydrodynamics (SPH) technique is a numerical method for solving gas-dynamical problems. It has been applied to simulate the evolution of a wide variety of astrophysical systems. The method has a second-order accuracy,…

天体物理仪器与方法 · 物理学 2016-08-08 Domingo García-Senz , Rubén M. Cabezón , José A. Escartín , Kevin Ebinger

We compare here several modern versions of SPH with a particular focus on the impact of gradient accuracy. We examine specifically an approximation to the "linearly exact" gradients (aLE) with standard SPH kernel gradients and with linearly…

天体物理仪器与方法 · 物理学 2026-02-10 S. Rosswog

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…

计算物理 · 物理学 2023-09-06 Zhentong Wang , Bo Zhang , Oskar J. Haidn , Xiangyu Hu

Basic Smoothed Particle Hydrodynamics (SPH) models exhibit excessive, numerical dissipation in the simulation of water wave propagation. This can be remedied using higher-order approaches such as kernel gradient correction, which introduce…

计算工程、金融与科学 · 计算机科学 2025-11-14 Lennart Justin Schulze , Vito Zago , Giuseppe Bilotta , Robert Anthony Dalrymple

We introduce adaptive particle refinement for compressible smoothed particle hydrodynamics (SPH). SPH calculations have the natural advantage that resolution follows mass, but this is not always optimal. Our implementation allows the user…

天体物理仪器与方法 · 物理学 2024-09-19 Rebecca Nealon , Daniel Price

We reconsider randomized algorithms for the low-rank approximation of symmetric positive semi-definite (SPSD) matrices such as Laplacian and kernel matrices that arise in data analysis and machine learning applications. Our main results…

机器学习 · 计算机科学 2013-06-05 Alex Gittens , Michael W. Mahoney

We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation…

量子物理 · 物理学 2025-09-26 Rhonda Au-Yeung , Anthony J. Williams , Viv M. Kendon , Steven J. Lind

We describe novel subgradient methods for a broad class of matrix optimization problems involving nuclear norm regularization. Unlike existing approaches, our method executes very cheap iterations by combining low-rank stochastic…

机器学习 · 计算机科学 2012-07-03 Haim Avron , Satyen Kale , Shiva Kasiviswanathan , Vikas Sindhwani

We present and test a new, special-relativistic formulation of Smoothed Particle Hydrodynamics (SPH). Our approach benefits from several improvements with respect to earlier relativistic SPH formulations. It is self-consistently derived…

高能天体物理现象 · 物理学 2015-05-13 S. Rosswog

We provide improved error bounds for kernel-based numerical differentiation in terms of growth functions when kernels are of a finite smoothness, such as polyharmonic splines, thin plate splines or Wendland kernels. In contrast to existing…

数值分析 · 数学 2025-12-24 Oleg Davydov

The numerical accuracy of particle-based approximations in Smoothed Particle Hydrodynamics (SPH) is significantly affected by the spatial uniformity of particle distributions, especially for second-order derivatives. This study aims to…

数值分析 · 数学 2026-01-29 Zhonghua Qiao , Yifan Wei

We introduce a modified SPH approach that is based on discretising the particle density instead of the mass density. This approach makes it possible to use SPH particles with very different masses to simulate multi-phase flows with large…

计算物理 · 物理学 2016-09-08 Frank Ott , Erik Schnetter

We seek to accelerate and increase the size of simulations for fluid-structure interactions (FSI) by using multiple resolutions in the spatial discretization of the equations governing the time evolution of systems displaying two-way…

流体动力学 · 物理学 2017-08-02 Wei Hu , Wenxiao Pan , Milad Rakhsha , Qiang Tian , Haiyan Hu , Dan Negrut

In the standard SPH method, the interaction between two particles might be not pairwise when the support domain varies, which can result in a reduction of accuracy. To deal with this problem, a modified SPH approach is presented in this…

计算物理 · 物理学 2017-03-22 Zili Dai , Huilong Ren , Xiaoying Zhuang , Timon Rabczuk

In this paper we test a special-relativistic formulation of Smoothed Particle Hydrodynamics (SPH) that has been derived from the Lagrangian of an ideal fluid. Apart from its symmetry in the particle indices, the new formulation differs from…

天体物理仪器与方法 · 物理学 2010-05-12 S. Rosswog

We derive a new discretisation method for first order PDEs of arbitrary spatial dimension, which is based upon a meshfree spatial approximation. This spatial approximation is similar to the SPH (smoothed particle hydrodynamics) technique…

数值分析 · 数学 2016-01-25 Tobias Ramming , Holger Wendland
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