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The Finite Element Method (FEM) is a well-established procedure for computing approximate solutions to deterministic engineering problems described by partial differential equations. FEM produces discrete approximations of the solution with…

An $hp$ version of interface penalty finite element method ($hp$-IPFEM) is proposed for elliptic interface problems in two and three dimensions on unfitted meshes. Error estimates in broken $H^1$ norm, which are optimal with respect to $h$…

Numerical Analysis · Mathematics 2010-07-20 Haijun Wu , Yuanming Xiao

We propose a conservative energy method based on neural networks with subdomains for solving variational problems (CENN), where the admissible function satisfying the essential boundary condition without boundary penalty is constructed by…

Numerical Analysis · Mathematics 2023-01-12 Yizheng Wang , Jia Sun , Wei Li , Zaiyuan Lu , Yinghua Liu

We present the analytical formulation and the finite element solution of a fractional-order nonlocal continuum model of a Euler-Bernoulli beam. Employing consistent definitions for the fractional-order kinematic relations, the governing…

Numerical Analysis · Mathematics 2021-02-11 Sansit Patnaik , Sai Sidhardh , Fabio Semperlotti

We present a new Integrated Finite Element Neural Network framework (I-FENN), with the objective to accelerate the numerical solution of nonlinear computational mechanics problems. We leverage the swift predictive capability of neural…

Computational Engineering, Finance, and Science · Computer Science 2022-12-02 Panos Pantidis , Mostafa E. Mobasher

In this paper, we propose a linearized finite element method (FEM) for solving the cubic nonlinear Schr\"{o}dinger equation with wave operator. In this method, a modified leap-frog scheme is applied for time discretization and a Galerkin…

Numerical Analysis · Mathematics 2019-02-25 Wentao Cai , Dongdong He , Kejia Pan

The finite element methods (FEM) are important techniques in engineering for solving partial differential equations, but they depend heavily on element shape quality for stability and good performance. In this paper, we introduce the…

Numerical Analysis · Mathematics 2016-03-30 Rebecca Conley , Tristan J. Delaney , Xiangmin Jiao

We describe and analyze a hybrid finite element/neural network method for predicting solutions of partial differential equations. The methodology is designed for obtaining fine scale fluctuations from neural networks in a local manner. The…

Numerical Analysis · Mathematics 2026-02-24 Uladzislau Kapustsin , Utku Kaya , Johannes Pfefferer , Thomas Richter

We consider an interface problem often arising in transport problems: a coupled system of partial differential equations with one (elliptic) transport equation on a bounded domain and one equation (in this case the Laplace problem) on the…

Numerical Analysis · Mathematics 2016-05-24 Christoph Erath , Robert Schorr

We consider a hybrid FEM-BEM method to compute approximations of full-space linear elliptic transmission problems. First, we derive a priori and a posteriori error estimates. Then, building on the latter, we present an adaptive algorithm…

Numerical Analysis · Mathematics 2025-04-30 Gregor Gantner , Michele Ruggeri

This paper presents the Virtual Element Method (VEM) for the modeling of crack propagation in 2D within the context of linear elastic fracture mechanics (LEFM). By exploiting the advantage of mesh flexibility in the VEM, we establish an…

Computational Engineering, Finance, and Science · Computer Science 2018-08-02 Vien Minh Nguyen-Thanh , Xiaoying Zhuang , Hung Nguyen-Xuan , Timon Rabczuk , Peter Wriggers

We present a trust-region-based adaptive finite-element algorithm for numerically solving a class of nonsmooth PDE-constrained optimization problems that includes problems with sparsifying regularizers and convex constraints. In particular,…

Optimization and Control · Mathematics 2026-04-28 Harbir Antil , Robert J. Baraldi , Rohit Khandelwal , Drew P. Kouri

This paper is concerned with the analysis of a new stable space-time finite element method (FEM) for the numerical solution of parabolic evolution problems in moving spatial computational domains. The discrete bilinear form is elliptic on…

Numerical Analysis · Mathematics 2018-05-14 Stephen Edward Moore

We propose and analyze novel adaptive algorithms for the numerical solution of elliptic partial differential equations with parametric uncertainty. Four different marking strategies are employed for refinement of stochastic Galerkin finite…

Numerical Analysis · Mathematics 2019-10-08 Alex Bespalov , Dirk Praetorius , Leonardo Rocchi , Michele Ruggeri

In this paper, we discuss the application of the Generalized Finite Element Method (GFEM) to approximate the solutions of quasilinear elliptic equations with multiple interfaces in one dimensional space. The problem is characterized by…

Numerical Analysis · Mathematics 2021-02-02 Tilsa Aryeni , Quanling Deng , Victor Ginting

We present a framework for solving a broad class of ill-posed inverse problems governed by partial differential equations (PDEs), where the target coefficients of the forward operator are recovered through an iterative regularization scheme…

We develop a parametric cut finite element method for elliptic boundary value problems with corner singularities where we have weighted control of higher order derivatives of the solution to a neighborhood of a point at the boundary. Our…

Numerical Analysis · Mathematics 2019-06-04 Tobias Jonsson , Mats G. Larson , Karl Larsson

This paper introduces a new weak Galerkin (WG) finite element method for second order elliptic equations on polytopal meshes. This method, called WG-FEM, is designed by using a discrete weak gradient operator applied to discontinuous…

Numerical Analysis · Mathematics 2012-08-20 Lin Mu , Junping Wang , Xiu Ye

An adaptive moving mesh finite element method is proposed for the numerical solution of the regularized long wave (RLW) equation. A moving mesh strategy based on the so-called moving mesh PDE is used to adaptively move the mesh to improve…

Numerical Analysis · Mathematics 2018-01-09 Changna Lu , Weizhang Huang , Jianxian Qiu

In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equations (PDEs). However, many neural network-based methods…

Machine Learning · Computer Science 2024-04-24 Adrian Celaya , Keegan Kirk , David Fuentes , Beatrice Riviere