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We propose an efficient algorithm for the immersed boundary method on distributed-memory architectures, with the computational complexity of a completely explicit method and excellent parallel scaling. The algorithm utilizes the…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-01-29 Jeffrey K. Wiens , John M. Stockie

The simulation of hypersonic flows is computationally demanding due to large gradients of the flow variables caused by strong shock waves and thick boundary or shear layers. The resolution of those gradients imposes the use of extremely…

Computational Physics · Physics 2014-10-16 Sarah Frauholz , Arianna Bosco , Birgit U. Reinartz , Siegfried Müller , Marek Behr

In this paper, we first study nonsmooth steepest descent method for nonsmooth functions defined on Hilbert space and establish the corresponding algorithm by proximal subgradients. Then, we use this algorithm to find stationary points for…

Optimization and Control · Mathematics 2015-02-25 Zhou Wei , Qing Hai He

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS). In this paper, we propose a novel data analysis framework with…

Machine Learning · Statistics 2020-03-03 Yuka Hashimoto , Isao Ishikawa , Masahiro Ikeda , Fuyuta Komura , Takeshi Katsura , Yoshinobu Kawahara

We present a numerical method for studying the normal modes of accretion flows around black holes. In this first paper, we focus on two-dimensional, viscous, hydrodynamic disks, for which the linear modes have been calculated analytically…

Astrophysics · Physics 2009-11-10 Chi-kwan Chan , Dimitrios Psaltis , Feryal Ozel

This paper aims to numerically verify the large Reynolds number asymptotic theory of magneto-hydrodynamic (MHD) flows proposed in the companion paper Deguchi (2019). To avoid any complexity associated with the chaotic nature of turbulence…

Fluid Dynamics · Physics 2019-09-04 Kengo Deguchi

The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in physical principles. In this study, we focus on the…

Fluid Dynamics · Physics 2024-08-30 Samuel E. Otto , Cassio M. Oishi , Fabio Amaral , Steven L. Brunton , J. Nathan Kutz

We present a Lagrangian Heterogeneous Multiscale Method (LHMM) for simulating the non-Newtonian rheology of polymer melts in complex two-dimensional flows. The method couples Dissipative Particle Dynamics (DPD) at the microscale with a…

Fluid Dynamics · Physics 2025-12-19 Edgar A. Patiño-Nariño , Nicolas Moreno , Marco Ellero

By way of concrete presentations, we construct two infinite-dimensional transforms at the crossroads of Gaussian fields and reproducing kernel Hilbert spaces (RKHS), thus leading to a new infinite-dimensional Fourier transform in a general…

Functional Analysis · Mathematics 2023-03-31 Palle E. T. Jorgensen , Myung-Sin Song , James Feng Tian

We present a new magnetohydrodynamic (MHD) code for the simulation of wave propagation in the solar atmosphere, under the effects of electrical resistivity, but not dominant, and heat transference in a uniform 3D grid. The code is based on…

Solar and Stellar Astrophysics · Physics 2017-08-02 Anamaría Navarro , F. D. Lora-Clavijo , Guillermo A. González

In this work, we introduce a framework to design multidimensional Riemann solvers for nonlinear systems of hyperbolic conservation laws on general unstructured polygonal Voronoi-like tessellations. In this framework we propose two simple…

Numerical Analysis · Mathematics 2026-02-03 Elena Gaburro , Mario Ricchiuto , Michael Dumbser

This paper introduces the Fej\'er-monotone hybrid steepest descent method (FM-HSDM), a new member to the HSDM family of algorithms, for solving affinely constrained minimization tasks in real Hilbert spaces, where convex smooth and…

Optimization and Control · Mathematics 2018-04-11 Konstantinos Slavakis , Isao Yamada

New time integration methods are proposed for simulating incompressible multiphase flow in pipelines described by the one-dimensional two-fluid model. The methodology is based on 'half-explicit' Runge-Kutta methods, being explicit for the…

Numerical Analysis · Mathematics 2019-02-19 Benjamin Sanderse , Arthur E. P. Veldman

Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and commonly used in nonparametric…

Methodology · Statistics 2013-05-07 Robert Hable

The ideal Magnetohydrodynamics (MHD) equations are challenging because one needs to maintain the divergence free condition, $\nabla \cdot \Bv = 0$. Many numerical methods have been developed to enforce this condition. In this work, we…

Numerical Analysis · Mathematics 2019-08-06 Firat Cakir , Andrew Christlieb , Yan Jiang

An implicit algorithm for solving the equations of general relativistic hydrodynamics in conservative form in three-dimensional axi-symmetry is presented. This algorithm is a direct extension of the pseudo-Newtonian implicit radiative…

Astrophysics · Physics 2009-06-23 Ahmad Hujeirat , Max Camenzind , Bernhard W. Keil

In this paper we consider from two different aspects the proximal alternating direction method of multipliers (ADMM) in Hilbert spaces. We first consider the application of the proximal ADMM to solve well-posed linearly constrained…

Optimization and Control · Mathematics 2023-10-11 Qinian Jin

We discuss a short-time existence theorem of solutions to the initial value problem for a fourth-order dispersive flow for curves parametrized by the real line into a compact K\"ahler manifold. Our equations geometrically generalize a…

Analysis of PDEs · Mathematics 2013-08-27 Hiroyuki Chihara , Eiji Onodera

In this paper, we present an efficient numerical algorithm for solving the time-dependent Cahn--Hilliard--Navier--Stokes equations that model the flow of two phases with different densities. The pressure-correction step in the projection…

Numerical Analysis · Mathematics 2020-11-02 Chen Liu , Deep Ray , Christopher Thiele , Lu Lin , Beatrice Riviere

We study recursive regularized learning algorithms in the reproducing kernel Hilbert space (RKHS) with non-stationary online data streams. We introduce the concept of random Tikhonov regularization path and decompose the tracking error of…

Machine Learning · Computer Science 2025-10-24 Xiwei Zhang , Yan Chen , Tao Li
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