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In this paper, we apply the range-separated (RS) tensor format [6] for the construction of new regularization scheme for the Poisson-Boltzmann equation (PBE) describing the electrostatic potential in biomolecules. In our approach, we use…

Numerical Analysis · Mathematics 2024-12-20 Peter Benner , Venera Khoromskaia , Boris Khoromskij , Cleophas Kweyu , Matthias Stein

In this paper, we derive an a-posteriori error indicator for the Generalized Multiscale Finite Element Method (GMsFEM) framework. This error indicator is further used to develop an adaptive enrichment algorithm for the linear elliptic…

Numerical Analysis · Mathematics 2015-06-17 Eric T. Chung , Yalchin Efendiev , Guanliang Li

For time integration of transient eddy current problems commonly implicit time integration methods are used, where in every time step one or several nonlinear systems of equations have to be linearized with the Newton-Raphson method due to…

Computational Engineering, Finance, and Science · Computer Science 2017-09-26 Jennifer Dutiné , Markus Clemens , Sebastian Schöps , Georg Wimmer

This paper presents a space-time finite element method (FEM) based on an unfitted mesh for solving parabolic problems on moving domains. Unlike other unfitted space-time finite element approaches that commonly employ the discontinuous…

Numerical Analysis · Mathematics 2026-04-03 Ruizhi Wang , Weibing Deng

The Poisson-Boltzmann equation (PBE) is a fundamental implicit solvent continuum model for calculating the electrostatic potential of large ionic solvated biomolecules. However, its numerical solution encounters severe challenges arising…

Numerical Analysis · Mathematics 2021-03-02 Cleophas Kweyu , Lihong Feng , Matthias Stein , Peter Benner

The pseudopotential lattice Boltzmann method (LBM) is a prominent approach for simulating multiphase flows, valued for its physical intuitiveness and computational tractability. However, existing immiscible pseudopotential methods for…

Fluid Dynamics · Physics 2026-03-02 Yizhong Chen , Zhibin Wang

Solving partial differential equations is difficult. Recently proposed neural resolution-invariant models, despite their effectiveness and efficiency, usually require equispaced spatial points of data. However, sampling in spatial domain is…

Machine Learning · Computer Science 2023-03-21 Haitao Lin , Lirong Wu , Yongjie Xu , Yufei Huang , Siyuan Li , Guojiang Zhao , Stan Z. Li

Many new possibilities to observe and use novel physical effects are discovered at so called exceptional points (EPs). This is done by using parity-time (PT) -symmetric non-Hermitian systems and balancing gains and losses. When combined…

Applied Physics · Physics 2023-03-06 Jianlan Xie , Shaohua Dong , Bei Yan , Yuchen Peng , Jianjun Liu , Chengwei Qiu , Shuangchun Wen

We present a framework for solving partial different equations on evolving surfaces. Based on the grid-based particle method (GBPM) [18], the method can naturally resample the surface even under large deformation from the motion law. We…

Numerical Analysis · Mathematics 2024-07-25 Ningchen Ying , Shingyu Leung

We propose a Lawson-time-splitting extended Fourier pseudospectral (LTSeFP) method for the numerical integration of the Gross-Pitaevskii equation with time-dependent potential that is of low regularity in space. For the spatial…

Numerical Analysis · Mathematics 2025-04-29 Bo Lin , Ying Ma , Chushan Wang

In computational biochemistry and biophysics, understanding the role of electrostatic interactions is crucial for elucidating the structure, dynamics, and function of biomolecules. The Poisson-Boltzmann (PB) equation is a foundational tool…

Biomolecules · Quantitative Biology 2024-10-08 Yongxian Wu , Qiang Zhu , Ray Luo

The second paper of this series presents two robust entropy stable shock-capturing methods for discontinuous Galerkin spectral element (DGSEM) discretizations of the compressible magneto-hydrodynamics (MHD) equations. Specifically, we use…

Partial differential equations (PDEs) provide a mathematical foundation for simulating and understanding intricate behaviors in both physical sciences and engineering. With the growing capabilities of deep learning, data$-$driven approaches…

Machine Learning · Computer Science 2025-10-14 Narayan S Iyer , Bivas Bhaumik , Ram S Iyer , Satyasaran Changdar

Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as finite element methods, with solutions over the…

Methodology · Statistics 2024-02-15 Chih-Li Sung , Wenjia Wang , Liang Ding , Xingjian Wang

Elliptic Partial Differential Equations (PDEs) play a central role in computing the equilibrium conditions of physical problems (heat, gravitation, electrostatics, etc.). Efficient solutions to elliptic PDEs are also relevant to computer…

Graphics · Computer Science 2026-02-13 Zhiyuan Zhang , Amir Vaxman , Stefanos-Aldo Papanicolopulos , Kartic Subr

The aim of this paper is to present and validate two new procedures to enforce the Geometric Conservation Law (GCL) on a moving grid for an Arbitrary Lagrangian Eulerian (ALE) formulation of the Euler equations discretized in time for…

Computational Engineering, Finance, and Science · Computer Science 2019-07-24 Marc Benoit , Siva Nadarajah

We explore a new way to handle flux boundary conditions imposed on level sets. The proposed approach is a diffuse interface version of the shifted boundary method (SBM) for continuous Galerkin discretizations of conservation laws in…

Numerical Analysis · Mathematics 2022-05-11 Dmitri Kuzmin , Jan-Phillip Bäcker

In the present paper, a fluid-particle coupling method is directly derived from the Navier-Stokes equations (NSE) by applying the concept of volume-filtering, yielding a physically consistent methodology to incorporate solid wall boundary…

Fluid Dynamics · Physics 2024-10-17 Max Hausmann , Hani Elmestikawy , Berend van Wachem

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. Mixed generalized multiscale finite element method (GMsFEM)…

Numerical Analysis · Mathematics 2017-04-05 Lijian Jiang , Qiuqi Li

Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. However, existing GP approximations do not simultaneously support…

Machine Learning · Computer Science 2021-06-21 Will Tebbutt , Arno Solin , Richard E. Turner
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