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Material properties such as permeability fields in heterogeneous porous media are often represented as discontinuous, piecewise constant data tied to a given spatial discretization. Such representations are inherently mesh-dependent,…

Numerical Analysis · Mathematics 2026-05-19 Kapil Chawla , Sanghyun Lee , Yeonjong Shin

Adaptive Finite Element Method (adaptivity) is known to be an effective numerical tool for some ill-posed problems. The key advantage of the adaptivity is the image improvement with local mesh refinements. A rigorous proof of this property…

Mathematical Physics · Physics 2012-10-30 Larisa Beilina , Michael V. Klibanov

The paper introduces an adaptive version of the stabilized Trace Finite Element Method (TraceFEM) designed to solve low-regularity elliptic problems on level-set surfaces using a shape-regular bulk mesh in the embedding space. Two…

Numerical Analysis · Mathematics 2024-08-21 Timo Heister , Maxim A. Olshanskii , Vladimir Yushutin

The Equation-Free approach to efficient multiscale numerical computation marries trusted micro-scale simulations to a framework for numerical macro-scale reduction -- the patch dynamics scheme. A recent novel patch scheme empowered the…

Dynamical Systems · Mathematics 2021-08-27 John Maclean , J. E. Bunder , I. G. Kevrekidis , A. J. Roberts

In this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM). Experience shows that a na\"{i}ve marking policy leads to inefficient use of computational resources for adaptive mesh refinement…

Numerical Analysis · Mathematics 2023-12-27 Andrew Gillette , Brendan Keith , Socratis Petrides

Recently, neural networks have been extensively employed to solve partial differential equations (PDEs) in physical system modeling. While major studies focus on learning system evolution on predefined static mesh discretizations, some…

Machine Learning · Computer Science 2024-02-20 Peiyan Hu , Yue Wang , Zhi-Ming Ma

Localized features such as singularities, sharp gradients, discontinuities, and moving sources require adaptive finite element discretizations. Conventional refinement strategies introduce significant computational overhead through…

Computational Engineering, Finance, and Science · Computer Science 2026-04-29 Jan Niklas Schmäke , Martin Ruess

Deep learning and the collocation method are merged and used to solve partial differential equations describing structures' deformation. We have considered different types of materials: linear elasticity, hyperelasticity (neo-Hookean) with…

Machine Learning · Computer Science 2021-11-24 Diab W. Abueidda , Qiyue Lu , Seid Koric

Many problems in physics are inherently of multi-scale nature. The issues of MHD turbulence or magnetic reconnection, namely in the hot and sparse, almost collision-less astrophysical plasmas, can stand as clear examples. The Finite Element…

Computational Physics · Physics 2012-06-14 Jan Skala , Miroslav Barta

High-index saddle dynamics (HiSD) is an effective approach for computing saddle points of a prescribed Morse index and constructing solution landscapes for complex nonlinear systems. However, for problems with ill-conditioned Hessians…

Numerical Analysis · Mathematics 2026-05-25 Bingzhang Huang , Hua Su , Lei Zhang , Jin Zhao

In this paper, we propose a novel mesh-free numerical method for solving the elliptic interface problems based on deep learning. We approximate the solution by the neural networks and, since the solution may change dramatically across the…

Numerical Analysis · Mathematics 2020-05-12 Cuiyu He , Xiaozhe Hu , Lin Mu

A posteriori upper and lower bounds are derived for the linear finite element method (FEM) for the Helmholtz equation with large wave number. It is proved rigorously that the standard residual type error estimator seriously underestimates…

Numerical Analysis · Mathematics 2021-10-25 Songyao Duan , Haijun Wu

In this paper, we examine a number of additive and multiplicative multilevel iterative methods and preconditioners in the setting of two-dimensional local mesh refinement. While standard multilevel methods are effective for uniform…

Numerical Analysis · Mathematics 2010-01-12 Burak Aksoylu , Stephen Bond , Michael Holst

We present both $hp$-a priori and $hp$-a posteriori error analysis of a mixed-order hybrid high-order (HHO) method to approximate second-order elliptic problems on simplicial meshes. Our main result on the $hp$-a priori error analysis is a…

Numerical Analysis · Mathematics 2025-07-25 Zhaonan Dong , Alexandre Ern

Since the advent of mesh-free methods as a tool for the numerical analysis of systems of Partial Differential Equations (PDEs), many variants of differential operator approximation have been proposed. In this work, we propose a local…

Numerical Analysis · Mathematics 2022-03-07 Mitja Jančič , Gregor Kosec

In this paper, we develop a gradient recovery based linear (GRBL) finite element method (FEM) and a Hessian recovery based linear (HRBL) FEM for second order elliptic equations in non-divergence form. The elliptic equation is casted into a…

Numerical Analysis · Mathematics 2022-11-09 Minqiang Xu , Runchang Lin , Qingsong Zou

This paper presents a new parameter free partially penalized immersed finite element method and convergence analysis for solving second order elliptic interface problems. A lifting operator is introduced on interface edges to ensure the…

Numerical Analysis · Mathematics 2022-02-23 Haifeng Ji , Feng Wang , Jinru Chen , Zhilin Li

We explore a class of splitting schemes employing implicit-explicit (IMEX) time-stepping to achieve accurate and energy-stable solutions for thin-film equations and Cahn-Hilliard models with variable mobility. This splitting method…

Numerical Analysis · Mathematics 2024-05-31 Saulo Orizaga , Thomas Witelski

This paper presents an implicit solution formula for the Hamilton-Jacobi partial differential equation (HJ PDE). The formula is derived using the method of characteristics and is shown to coincide with the Hopf and Lax formulas in the case…

Machine Learning · Computer Science 2025-02-03 Yesom Park , Stanley Osher

The random feature method (RFM), a mesh-free machine learning-based framework, has emerged as a promising alternative for solving PDEs on complex domains. However, for large three-dimensional nonlinear problems, attaining high accuracy…

Numerical Analysis · Mathematics 2025-10-07 Longze Tan
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