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The Lattice Boltzmann Method (LBM) has emerged as a powerful tool in computational fluid dynamics and material science. However, standard LBM formulation imposes some limitations on the applications of the method, particularly compressible…

软凝聚态物质 · 物理学 2025-03-14 Navid Afrasiabian , Colin Denniston

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that a particle trajectory generated in a forward simulation…

图形学 · 计算机科学 2024-05-17 Junwei Zhou , Duowen Chen , Molin Deng , Yitong Deng , Yuchen Sun , Sinan Wang , Shiying Xiong , Bo Zhu

We propose an accelerated computational fluid dynamics framework based on a hybrid Fourier Neural Operator-Lattice Boltzmann Method (FNO-LBM) for steady and unsteady weakly compressible flows. FNO-based initialization significantly…

流体动力学 · 物理学 2026-05-01 Alexandra Junk , Josef M. Winter , Meike Tütken , Steffen Schmidt , Nikolaus A. Adams

An implementation of the fast multiple method (FMM) is performed for magnetic systems with long-ranged dipolar interactions. Expansion in spherical harmonics of the original FMM is replaced by expansion of polynomials in Cartesian…

计算物理 · 物理学 2015-05-13 Wen Zhang , Stephan Haas

This work illustrates the possibility to apply the Fast Fourier Transformation to obtain the integrals of the Boundary Element Method (BEM) on arbitrary shapes. The procedure is inspired by the technique used with great success within the…

计算物理 · 物理学 2018-09-05 Justus Benad

We present an accurate Lagrangian method based on vortex particles, level-sets, and immersed boundary methods, for animating the interplay between two fluids and rigid solids. We show that a vortex method is a good choice for simulating…

The emergence and understanding of new design paradigms that exploit flow induced mechanical instabilities for propulsion or energy harvesting demands robust and accurate flow structure interaction numerical models. In this context, we…

流体动力学 · 物理学 2019-05-22 Caroline Bernier , Mattia Gazzola , Renaud Ronsse , Philippe Chatelain

Wavelet decompositions of integral operators have proven their efficiency in reducing computing times for many problems, ranging from the simulation of waves or fluids to the resolution of inverse problems in imaging. Unfortunately,…

图像与视频处理 · 电气工程与系统科学 2020-08-03 Paul Escande , Pierre Weiss

Astrophysical turbulent flows display an intrinsically multi-scale nature, making their numerical simulation and the subsequent analyses of simulated data a complex problem. In particular, two fundamental steps in the study of turbulent…

天体物理仪器与方法 · 物理学 2024-07-10 David Vallés-Pérez , Susana Planelles , Vicent Quilis , Frederick Groth , Tirso Marin-Gilabert , Klaus Dolag

Kernel methods are a highly effective and widely used collection of modern machine learning algorithms. A fundamental limitation of virtually all such methods are computations involving the kernel matrix that naively scale quadratically…

机器学习 · 计算机科学 2021-06-09 John Paul Ryan , Sebastian Ament , Carla P. Gomes , Anil Damle

This paper is a short guideline to the decomposition of a compressible velocity into vortical and compressible structures using standard flow solvers. In particular, this is a fast solution to get an idea of the compressible fields inside…

流体动力学 · 物理学 2023-08-10 Stefan Schoder

We further develop a thermal LB model for multiphase flows. In the improved model, we propose to use the FFT scheme to calculate both the convection term and external force term. The usage of FFT scheme is detailed and analyzed. By using…

软凝聚态物质 · 物理学 2015-05-20 Yanbiao Gan , Aiguo Xu , Guangcai Zhang , Yingjun Li

We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich data base of machine learning control (MLC) optimizing a feedback law for…

流体动力学 · 物理学 2020-09-01 Hao Li , Guy Y. Cornejo Maceda , Yiqing Li , Jianguo Tan , Marek Morzyński , Bernd R. Noack

We study the $h$- and $p$-versions of non-conforming harmonic virtual element methods (VEM) for the approximation of the Dirichlet-Laplace problem on a 2D polygonal domain, providing quasi-optimal error bounds. Harmonic VEM do not make use…

数值分析 · 数学 2018-07-30 Lorenzo Mascotto , Ilaria Perugia , Alexander Pichler

How heterogeneous multiscale methods (HMM) handle fluctuations acting on the slow variables in fast-slow systems is investigated. In particular, it is shown via analysis of central limit theorems (CLT) and large deviation principles (LDP)…

概率论 · 数学 2016-01-12 David Kelly , Eric Vanden-Eijnden

The formation of the leading-edge vortex (LEV) is a key feature of unsteady flows past aerodynamic surfaces, but is expensive to model in high fidelity computations. Low-order methods based on discrete vortex elements are able to capture…

流体动力学 · 物理学 2022-06-24 Pedro Hernandez Gelado , Kiran Kumar Ramesh

This article provides next step towards solving speed bottleneck of any system that intensively uses convolutions operations (e.g. CNN). Method described in the article is applied on deformable part models (DPM) algorithm. Method described…

计算机视觉与模式识别 · 计算机科学 2017-07-12 D. V. Parkhomenko , I. L. Mazurenko

We introduce a novel virtual element method (VEM) for the two dimensional Helmholtz problem endowed with impedance boundary conditions. Local approximation spaces consist of Trefftz functions, i.e., functions belonging to the kernel of the…

数值分析 · 数学 2018-10-26 L. Mascotto , I. Perugia , A. Pichler

This paper develops an efficient computational technique to assess the landslide responses to tectonic deformation and to predict the implications of large bedrocks landslides on the short and long-term development of the disasters. The…

数值分析 · 数学 2025-04-22 Eric Ngondiep

As large language models (LLMs) become increasingly powerful, the sequential nature of autoregressive generation creates a fundamental throughput bottleneck that limits the practical deployment. While Multi-Token Prediction (MTP) has…

机器学习 · 计算机科学 2025-09-24 Yuxuan Cai , Xiaozhuan Liang , Xinghua Wang , Jin Ma , Haijin Liang , Jinwen Luo , Xinyu Zuo , Lisheng Duan , Yuyang Yin , Xi Chen