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In this work, we review the framework of the Virtual Element Method (VEM) for a model in magneto-hydrodynamics (MHD), that incorporates a coupling between electromagnetics and fluid flow, and allows us to construct novel discretizations for…

Numerical Analysis · Mathematics 2021-04-12 Sebastian Naranjo-Alvarez , Vrushali Bokil , Vitaliy Gyrya , Gianmarco Manzini

The numerical approximation of 2D elasticity problems is considered, in the framework of the small strain theory and in connection with the mixed Hellinger-Reissner variational formulation. A low-order Virtual Element Method (VEM) with…

Numerical Analysis · Mathematics 2017-10-11 E. Artioli , S. de Miranda , C. Lovadina , L. Patruno

We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we…

Computational Engineering, Finance, and Science · Computer Science 2024-02-13 Bahador Bahmani , WaiChing Sun

In this paper we study the use of Virtual Element method for geomechanics. Our emphasis is on applications to reservoir simulations. The physical processes that form the reservoirs, such as sedimentation, erosion and faulting, lead to…

Numerical Analysis · Mathematics 2017-02-09 Odd Andersen , Halvor M. Nilsen , Xavier Raynaud

We present an hp-adaptive virtual element method (VEM) based on the hypercircle method of Prager and Synge for the approximation of solutions to diffusion problems. We introduce a reliable and efficient a posteriori error estimator, which…

Numerical Analysis · Mathematics 2021-11-30 Franco Dassi , Joscha Gedicke , Lorenzo Mascotto

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition,…

Computational Engineering, Finance, and Science · Computer Science 2019-11-07 Andrea Mendizabal , Pablo Márquez-Neila , Stéphane Cotin

This work proposes a general learned proximal alternating minimization algorithm, LPAM, for solving learnable two-block nonsmooth and nonconvex optimization problems. We tackle the nonsmoothness by an appropriate smoothing technique with…

Optimization and Control · Mathematics 2026-03-10 Yunmei Chen , Lezhi Liu , Lei Zhang

This work studies the approximation of plane problems concerning transversely isotropic elasticity, using a low-order Virtual Element Method (VEM), with a focus on near-incompressibility and near-inextensibility. Additionally, both…

Numerical Analysis · Mathematics 2019-03-06 D. van Huyssteen , B. D. Reddy

It is well known that the solution of topology optimization problems may be affected both by the geometric properties of the computational mesh, which can steer the minimization process towards local (and non-physical) minima, and by the…

Numerical Analysis · Mathematics 2016-12-28 Paola F. Antonietti , Matteo Bruggi , Simone Scacchi , Marco Verani

This work provides an efficient virtual element scheme for the modeling of nonlinear elastodynamics undergoing large deformations. The virtual element method (VEM) has been applied to various engineering problems such as elasto-plasticity,…

Numerical Analysis · Mathematics 2020-02-10 M. Cihan , F. Aldakheel , B. Hudobivnik , P. Wriggers

This paper summarizes the development of Veamy, an object-oriented C++ library for the virtual element method (VEM) on general polygonal meshes, whose modular design is focused on its extensibility. The linear elastostatic and Poisson…

We describe the development of machine-learned potentials of atmospheric gases with flexible monomers for molecular simulations. A recently suggested permutationally invariant polynomial neural network (PIP-NN) approach is utilized to…

Chemical Physics · Physics 2025-04-21 Artem Finenko

This document contains working annotations on the Virtual Element Method (VEM) for the approximate solution of diffusion problems with variable coefficients. To read this document you are assumed to have familiarity with concepts from the…

Numerical Analysis · Mathematics 2016-12-30 Gianmarco Manzini

In this work we present an extension of the Virtual Element Method with curved edges for the numerical approximation of the second order wave equation in a bidimensional setting. Curved elements are used to describe the domain boundary, as…

Numerical Analysis · Mathematics 2021-06-14 Franco Dassi , Alessio Fumagalli , Ilario Mazzieri , Anna Scotti , Giuseppe Vacca

We propose a combined nodal integration and virtual element method for compressible and nearly incompressible elasticity, wherein the strain is averaged at the nodes from the strain of surrounding virtual elements. For the strain averaging…

In this paper, we develop a high-order adaptive virtual element method (VEM) to simulate the self-consistent field theory (SCFT) model in arbitrary domains. The VEM is very flexible in handling general polygon elements and can treat hanging…

Numerical Analysis · Mathematics 2021-06-15 Huayi Wei , Xin Wang , Chunyu Chen , Kai Jiang

In this paper, we explore the use of the Virtual Element Method concepts to solve scalar and system hyperbolic problems on general polygonal grids. The new schemes stem from the active flux approach \cite{AF1}, which combines the usage of…

Numerical Analysis · Mathematics 2025-10-07 Rémi Abgrall , Yongle Liu , Walter Boscheri

Complex mechanic systems simulation is important in many real-world applications. The de-facto numeric solver using Finite Element Method (FEM) suffers from computationally intensive overhead. Though with many progress on the reduction of…

Machine Learning · Computer Science 2024-09-04 Jiasheng Shi , Fu Lin , Weixiong Rao

Periodic micromagnetic finite element method (PM-FEM) is introduced to solve periodic unit cell problems using the Landau-Lifshitz-Gilbert equation. PM-FEM is applicable to general problems with 1D, 2D, and 3D periodicities. PM-FEM is based…

Numerical Analysis · Mathematics 2024-09-24 Fangzhou Ai , Jiawei Duan , Vitaliy Lomakin

We present a methodology combining neural networks with physical principle constraints in the form of partial differential equations (PDEs). The approach allows to train neural networks while respecting the PDEs as a strong constraint in…

Numerical Analysis · Mathematics 2021-09-06 Sebastian K. Mitusch , Simon W. Funke , Miroslav Kuchta
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