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In this paper, we propose a Galerkin finite element method for the elliptic optimal control problem governed by the Riesz space-fractional PDEs on 2D domains with control variable being discretized by variational discretization technique.…

Numerical Analysis · Mathematics 2022-02-22 Xiaogang Zhu

In this paper, we introduce a numerical solution of a stochastic partial differential equation (SPDE) of elliptic type using polynomial chaos along side with polynomial approximation at Sinc points. These Sinc points are defined by a…

Numerical Analysis · Mathematics 2019-04-08 Maha Youssef , Roland Pulch

This work proposes a Variational Physics-Informed Neural Network (VPINN) framework that integrates the Petrov-Galerkin formulation with deep neural networks (DNNs) for solving one-dimensional singularly perturbed boundary value problems…

Numerical Analysis · Mathematics 2025-09-17 Vijay Kumar , Gautam Singh

This article proposes and analyzes the generalized weak Galerkin ({\rm g}WG) finite element method for the second order elliptic problem. A generalized discrete weak gradient operator is introduced in the weak Galerkin framework so that the…

Numerical Analysis · Mathematics 2023-05-16 Dan Li , Chunmei Wang , Junping Wang , Xiu Ye

This paper theoretically reanalyzes the convergence of the mini-batch stochastic gradient descent (SGD) for a structured minimization problem involving a finite-sum function with its gradient being stochastically approximated, and an…

Optimization and Control · Mathematics 2026-04-07 Runze Li , Jintao Xu , Wenxun Xing

We investigate reduced-order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the…

Numerical Analysis · Mathematics 2024-06-21 Philipp Weder , Mariella Kast , Fernando Henríquez , Jan S. Hesthaven

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

In this paper we develop an $hp$-adaptive procedure for the numerical solution of general, semilinear elliptic boundary value problems in 1d, with possible singular perturbations. Our approach combines both a prediction-type adaptive Newton…

Numerical Analysis · Mathematics 2018-03-07 Mario Amrein , Jens M. Melenk , Thomas P. Wihler

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature,…

Numerical Analysis · Mathematics 2024-02-07 Fabio Nobile , Thomas Trigo Trindade

Sparse-grid methods have recently gained interest in reducing the computational cost of solving high-dimensional kinetic equations. In this paper, we construct adaptive and hybrid sparse-grid methods for the Vlasov-Poisson-Lenard-Bernstein…

Motivated by applications to numerical simulation of flows in highly heterogeneous porous media, we develop multiscale finite element methods for second order elliptic equations. We discuss a multiscale model reduction technique in the…

Numerical Analysis · Mathematics 2015-06-15 Y. Efendiev , J. Galvis , R. Lazarov , M. Moon , M. Sarkis

We develop a new $C^{\,0}$-continuous Petrov-Galerkin spectral element method for one-dimensional fractional elliptic problems of the form ${}_{0}{\mathcal{D}}_{x}^{\alpha} u(x) - \lambda u(x) = f(x)$, $\alpha \in (1,2]$, subject to…

Numerical Analysis · Mathematics 2017-10-11 Ehsan Kharazmi , Mohsen Zayernouri , George Em Karniadakis

We present a new approach to using neural networks to approximate the solutions of variational equations, based on the adaptive construction of a sequence of finite-dimensional subspaces whose basis functions are realizations of a sequence…

Machine Learning · Computer Science 2021-06-01 Mark Ainsworth , Justin Dong

In this paper, we propose a pressure-robust weak Galerkin (WG) finite element scheme to solve the Stokes-Darcy problem. To construct the pressure-robust numerical scheme, we use the divergence-free velocity reconstruction operator to modify…

Numerical Analysis · Mathematics 2024-08-13 Jiwei Jia , Lin Yang , Qilong Zhai

We propose a generalized multiscale finite element method (GMsFEM) based on clustering algorithm to study the elliptic PDEs with random coefficients in the multi-query setting. Our method consists of offline and online stages. In the…

Numerical Analysis · Mathematics 2018-08-01 Eric T. Chung , Yalchin Efendiev , Wing Tat Leung , Zhiwen Zhang

A discontinuous Galerkin (DG) scheme for solving semilinear elliptic problem is developed and analyzed in this paper. The DG finite element discretizations are established, and the corresponding existence and uniqueness theorem is proved by…

Numerical Analysis · Mathematics 2021-01-27 Jiajun Zhan , Liuqiang Zhong , Jie Peng

This paper proposes a weak Galerkin (WG) finite element method for elliptic interface problems defined on nonconvex polygonal partitions. The method features a built-in stabilizer and retains a simple, symmetric, and positive definite…

Numerical Analysis · Mathematics 2025-12-23 Chunmei Wang , Shangyou Zhang

In this work, we present a novel error analysis for recovering a spatially dependent diffusion coefficient in an elliptic or parabolic problem. It is based on the standard regularized output least-squares formulation with an $H^1(\Omega)$…

Numerical Analysis · Mathematics 2020-10-07 Bangti Jin , Zhi Zhou

The weak Galerkin (WG) finite element method is an effective and flexible general numerical technique for solving partial differential equations. It is a natural extension of the classic conforming finite element method for discontinuous…

Numerical Analysis · Mathematics 2020-04-29 Xiu Ye , Shangyou Zhang

This paper deals with the identification of linear stochastic dynamical systems, where the unknowns include system coefficients and noise variances. Conventional approaches that rely on the maximum likelihood estimation (MLE) require…

Machine Learning · Statistics 2025-08-18 Jinwen Xu , Qin Lu , Yaakov Bar-Shalom