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Given an arbitrary function in H(div), we show that the error attained by the global-best approximation by H(div)-conforming piecewise polynomial Raviart-Thomas-N\'ed\'elec elements under additional constraints on the divergence and normal…

Numerical Analysis · Mathematics 2020-05-20 Alexandre Ern , Thirupathi Gudi , Iain Smears , Martin Vohralík

We analyze constrained and unconstrained minimization problems on patches of tetrahedra sharing a common vertex with discontinuous piecewise polynomial data of degree p. We show that the discrete minimizers in the spaces of piecewise…

Numerical Analysis · Mathematics 2024-07-29 T. Chaumont-Frelet , M. Vohralik

On the reference tetrahedron $\widehat K$, we define three projection-based interpolation operators on $H^2(\widehat K)$, ${\mathbf H}^1(\widehat K,\operatorname{\mathbf{curl}})$, and ${\mathbf H}^1(\widehat K,\operatorname{div})$. These…

Numerical Analysis · Mathematics 2020-01-27 Jens Markus Melenk , Claudio Rojik

We design a quasi-interpolation operator from the Sobolev space $H^1_0(\Omega)$ to its finite-dimensional finite element subspace formed by piecewise polynomials on a simplicial mesh with a computable approximation constant. The operator 1)…

Numerical Analysis · Mathematics 2025-07-17 T. Chaumont-Frelet , M. Vohralik

We devise and analyse a novel $\boldsymbol{H}(\textbf{curl})$-reconstruction operator for piecewise polynomial fields on shape-regular simplicial meshes. The (non-polynomial) reconstruction is devised over the mesh vertex patches using the…

Numerical Analysis · Mathematics 2026-03-23 Zhaonan Dong , Alexandre Ern

We present a local construction of H(curl)-conforming piecewise polynomials satisfying a prescribed curl constraint. We start from a piecewise polynomial not contained in the H(curl) space but satisfying a suitable orthogonality property.…

Numerical Analysis · Mathematics 2022-08-23 Théophile Chaumont-Frelet , Martin Vohralík

In this work, we propose a stabilization-free virtual element method for genreal order $\mathbf{H}(\operatorname{\mathbf{curl}})$ and $\mathbf{H}(\operatorname{div})$-conforming spaces. By the exact sequence of node, edge and face virtual…

Numerical Analysis · Mathematics 2025-01-28 Yuxuan Liao , Xue Feng , Yidong Huang

This paper introduces a quasi-interpolation operator for scalar- and vector-valued finite element spaces constructed on affine, shape-regular meshes with some continuity across mesh interfaces.This operator gives optimal estimates of the…

Numerical Analysis · Mathematics 2016-10-07 Alexandre Ern , Jean-Luc Guermond

We introduce computable projection operators onto piecewise polynomial spaces, defined via sampling and discrete least-squares polynomial approximations. The resulting mappings exhibit (almost) optimal approximation properties in $L^2$ and…

Numerical Analysis · Mathematics 2026-02-05 Johannes Storn

We derive $H_{\text{curl}}$-error estimates and improved $L^2$-error estimates for the Maxwell equations approximated using edge finite elements. These estimates only invoke the expected regularity pickup of the exact solution in the scale…

Numerical Analysis · Mathematics 2017-10-17 Alexandre Ern , Jean-Luc Guermond

In this paper we prove an optimal error estimate for the H(curl)-conforming projection based p-interpolation operator introduced in [L. Demkowicz and I. Babuska, p interpolation error estimates for edge finite elements of variable order in…

Numerical Analysis · Mathematics 2009-03-27 Alexei Bespalov , Norbert Heuer

We prove that the minimizer in the N\'ed\'elec polynomial space of some degree p > 0 of a discrete minimization problem performs as well as the continuous minimizer in H(curl), up to a constant that is independent of the polynomial degree…

Numerical Analysis · Mathematics 2020-06-03 Théophile Chaumont-Frelet , Alexandre Ern , Martin Vohralík

Projection operators arise naturally as one-particle density operators associated to Slater determinants in fields such as quantum mechanics and the study of determinantal processes. In the context of the semiclassical approximation of…

Mathematical Physics · Physics 2024-05-29 Laurent Lafleche

This article addresses two topics of significant mathematical and practical interest in the theory of kernel approximation: the existence of local and stable bases and the L_p--boundedness of the least squares operator. The latter is an…

Classical Analysis and ODEs · Mathematics 2011-03-10 Thomas Hangelbroek , Fran J Narcowich , Xingping Sun , Joe D Ward

In this article we analyze the spectral properties of the curl operator on closed Riemannian 3-manifolds. Specifically, we study metrics that are optimal in the sense that they minimize the first curl eigenvalue among any other metric of…

Differential Geometry · Mathematics 2026-02-13 Alberto Enciso , Wadim Gerner , Daniel Peralta-Salas

We devise local projection stabilization (LPS) methods for advection problems in the $\boldsymbol{H}$(curl) and $\boldsymbol{H}$(div) spaces, employing conforming finite element spaces of arbitrary order within a unified framework. The key…

Numerical Analysis · Mathematics 2025-05-23 Yangfan Luo , Jindong Wang , Shuonan Wu

In this paper we construct a new H(div)-conforming projection-based p-interpolation operator that assumes only $H^r(K) \cap \tilde H^{-1/2}(div,K)$-regularity (r > 0) on the reference element K (either triangle or square). We show that this…

Numerical Analysis · Mathematics 2009-10-21 Alexei Bespalov , Norbert Heuer

We study extensions of piecewise polynomial data prescribed in a patch of tetrahedra sharing an edge. We show stability in the sense that the minimizers over piecewise polynomial spaces with prescribed tangential component jumps across…

Numerical Analysis · Mathematics 2021-05-04 Théophile Chaumont-Frelet , Alexandre Ern , Martin Vohralík

We study the proximity operator of the nonconvex, scale-invariant ratio $h(\vx)=\|\vx\|_{1}/\|\vx\|_{2}$ and show it can be computed exactly in any dimension. By expressing $\vx=r\vu$ and exploiting sign and permutation invariance, we…

Optimization and Control · Mathematics 2026-01-23 Lixin Shen , Guohui Song

Neural operators have emerged as a powerful tool for learning mappings between infinite-dimensional function spaces. However, their approximation properties in Sobolev norms remain poorly quantified, even though these norms control both…

Machine Learning · Computer Science 2026-05-12 Nicole Hao
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