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In this work, we present a new stabilization method aimed at removing spurious oscillations in the pressure approximation of Biot's model for poroelasticity with low permeabilities and/or small time steps. We consider different…

Numerical Analysis · Mathematics 2024-07-30 Álvaro Pé de la Riva , Francisco J. Gaspar , Xiaozhe Hu , James Adler , Carmen Rodrigo , Ludmil Zikatanov

Simulations of many rigid bodies colliding with each other sometimes yield particularly interesting results if the colliding objects differ significantly in size and are non-spherical. The most expensive part within such a simulation code…

Computational Geometry · Computer Science 2022-10-21 Peter J. Noble , Tobias Weinzierl

This paper develops and analyzes a fully discrete finite element method for a class of semilinear stochastic partial differential equations (SPDEs) with multiplicative noise. The nonlinearity in the diffusion term of the SPDEs is assumed to…

Numerical Analysis · Mathematics 2018-11-22 Xiaobing Feng , Yukun Li , Yi Zhang

In this paper we propose a novel and general approach to design semi-implicit methods for the simulation of fluid-structure interaction problems in a fully Eulerian framework. In order to properly present the new method, we focus on the…

Numerical Analysis · Mathematics 2023-10-31 Mirco Ciallella , Thomas Milcent

In this paper we propose new numerical algorithms in the setting of unconstrained optimization problems and we study the rate of convergence in the iterates of the objective function. Furthermore, our algorithms are based upon splitting and…

Optimization and Control · Mathematics 2020-02-11 Cristian Daniel Alecsa

We introduce and analyze a Statically Condensed Iterated Penalty (SCIP) method for solving incompressible flow problems discretized with $p$th-order Scott-Vogelius elements. While the standard iterated penalty method is often the preferred…

Numerical Analysis · Mathematics 2023-01-06 Mark Ainsworth , Charles Parker

In this work, we propose a nonlinear stabilization technique for scalar conservation laws with implicit time stepping. The method relies on an artificial diffusion method, based on a graph-Laplacian operator. It is nonlinear, since it…

Numerical Analysis · Computer Science 2016-12-23 Santiago Badia , Jesús Bonilla

When solving partial differential equations (PDEs) using finite difference or finite element methods, efficient solvers are required for handling large sparse linear systems. In this paper, a recursive sparse LU decomposition for matrices…

Numerical Analysis · Mathematics 2024-08-27 Zhu Xuanru , Lai Jun

The method of regularized stokeslets is extensively used in biological fluid dynamics due to its conceptual simplicity and meshlessness. This simplicity carries a degree of cost in computational expense and accuracy because the number of…

Fluid Dynamics · Physics 2018-02-14 David J. Smith

We analyze the semi-implicit scheme of high-index saddle dynamics, which provides a powerful numerical method for finding the any-index saddle points and constructing the solution landscape. Compared with the explicit schemes of saddle…

Numerical Analysis · Mathematics 2023-10-10 Yue Luo , Lei Zhang , Pingwen Zhang , Zhiyi Zhang , Xiangcheng Zheng

We develop a nonlinear multigrid method to solve the steady state of microflow, which is modeled by the high order moment system derived recently for the steady-state Boltzmann equation with ES-BGK collision term. The solver adopts a…

Numerical Analysis · Mathematics 2014-12-15 Zhicheng Hu , Ruo Li

In this paper, an efficient solver for the Helmholtz equation using a noval approximation space is developed. The ingradients of the method include the approximation space recently proposed, a discontinuous Galerkin scheme extensively used,…

Numerical Analysis · Mathematics 2025-12-11 Shuhai Zhao

Paper presents a new solver for numerical solution of the Boltzmann kinetic equation with Shakhov model collision integral (S-model) for arbitrary spatial domains. Numerical method utilizes Tensor-Train decomposition, which allows to reduce…

Numerical Analysis · Mathematics 2021-04-13 A. V. Chikitkin , E. K. Kornev , V. A. Titarev

We present a new compatible finite element advection scheme for the compressible Euler equations. Unlike the discretisations described in Cotter and Kuzmin (2016) and Shipton et al (2018), the discretisation uses the lowest-order family of…

Numerical Analysis · Mathematics 2019-05-22 Thomas M. Bendall , Colin J. Cotter , Jemma Shipton

A system of semi-discrete coupled nonlinear Schr\"{o}dinger equations is studied. To show the complete integrability of the model with multiple components, we extend the discrete version of the inverse scattering method for the…

solv-int · Physics 2007-05-23 T. Tsuchida , H. Ujino , M. Wadati

We study the task of smoothing a circuit, i.e., ensuring that all children of a plus-gate mention the same variables. Circuits serve as the building blocks of state-of-the-art inference algorithms on discrete probabilistic graphical models…

Artificial Intelligence · Computer Science 2019-10-29 Andy Shih , Guy Van den Broeck , Paul Beame , Antoine Amarilli

We have undertaken an algorithmic search for new integrable semi-discretizations of physically relevant nonlinear partial differential equations. The search is performed by using a compatibility condition for the discrete Lax operators and…

Exactly Solvable and Integrable Systems · Physics 2017-05-18 Sylvie A. Bronsard , Dmitry E. Pelinovsky

When solving the first-order form of the linear Boltzmann equation, a common misconception is that the matrix-free computational method of ``sweeping the mesh", used in conjunction with the Discrete Ordinates method, is too complex or does…

Computational Physics · Physics 2020-04-07 Jan I C Vermaak , Jean C Ragusa , Jim E Morel

We design a variational asymptotic preserving scheme for the Vlasov-Poisson-Fokker-Planck system with the high field scaling, which describes the Brownian motion of a large system of particles in a surrounding bath. Our scheme builds on an…

Numerical Analysis · Mathematics 2020-12-17 Jose A. Carrillo , Li Wang , Wuzhe Xu , Ming Yan

We introduce a novel kernel learning framework toward efficiently solving nonlinear partial differential equations (PDEs). In contrast to the state-of-the-art kernel solver that embeds differential operators within kernels, posing…

Machine Learning · Computer Science 2025-06-09 Zhitong Xu , Da Long , Yiming Xu , Guang Yang , Shandian Zhe , Houman Owhadi