#finite element methods

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8 papers match

cs.CE2026

Data-free neural PDE solvers based on Graph Neural Networks and weak forms

Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1

The paper introduces a neural network that solves partial differential equations without any training data by using a graph neural network and the weak form of the equations, compu…

#graph neural networks#physics-informed neural networks#partial differential equations#weak formulation
math.NA2026

R3MG-C: a high-order algebraic-geometric multilevel preconditioner for continuous finite element discretizations

Davide Polverino, Marco Feder, Luca Heltai

The paper proposes an algebraic‑geometric multilevel preconditioner that builds a high‑order coarse space from finite element support points using an R‑tree partitioning, enabling…

#algebraic multigrid#finite element methods#preconditioning#high-order discretizations
math.NA2026

Fully discrete least-squares splitting scheme for the Monge-Ampère equation: finite element analysis and convergence

Anna Peruso

The paper introduces a fully discrete finite element framework for the two‑dimensional Dirichlet Monge‑Ampère equation using a least‑squares splitting algorithm, and provides conve…

#monge-ampere equation#finite element methods#least-squares splitting#convergence analysis
math.NA2026

Quasi-optimal polytopal finite element methods for biharmonic equation

Ngoc Tien Tran

The paper derives quasi‑optimal and lower‑order error bounds for several finite element schemes—including weak Galerkin, discontinuous Galerkin, and hybrid‑high order—applied to th…

#finite element methods#biharmonic equation#polytopal meshes#error estimation
math.NA2026

A structure-preserving Numerical Method for the Compressible Resistive-Hall-MHD System

Murtazo Nazarov, Rafael Rodriguez-Velasco, Ignacio Tomas

The paper introduces a finite element method that preserves physical properties for simulating compressible resistive Hall magnetohydrodynamics, combining explicit SSP‑RK for fluid…

#magnetohydrodynamics#hall effect#finite element methods#structure-preserving algorithms
math.NA2026

Tensor-Network Finite Elements for Analytic Operator Equations

Abhijatmedhi Chotrattanapituk, Michael J. Landry, Chu-Liang Fu +1

The paper introduces a framework that combines finite-element discretization with tensor-network representations to solve analytic operator equations, converting nonlinear PDEs int…

#finite element methods#tensor networks#operator equations#partial differential equations
math.NA2026

Sufficient conditions for strong discrete maximum principles in finite element solutions of linear and semilinear elliptic equations

Andrei Draganescu, L. Ridgway Scott

The paper presents a new technique to establish global strong discrete maximum principles for finite element solutions of linear and semilinear elliptic equations, even when standa…

#finite element methods#elliptic equations#discrete maximum principle#semilinear PDEs
math.NA2026

Broken-space Additive Schwarz Mass Inverse Approximations and (Block) Preconditioning

Oliver A. Krzysik, Ben S. Southworth, Golo A. Wimmer

The paper proposes a broken-space additive Schwarz (BRAS) method to approximate the inverse of finite-element mass matrices, showing it improves conditioning and reduces solve time…

#finite element methods#mass matrix preconditioning#additive schwarz#high-order elements

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