TY - JOUR

T1 - Projection in negative norms and the regularization of rough linear functionals

AU - Millar, F.

AU - Muga, I.

AU - Rojas, S.

AU - Van der Zee, K. G.

N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

PY - 2022/4

Y1 - 2022/4

N2 - In order to construct regularizations of continuous linear functionals acting on Sobolev spaces such as W01,q(Ω), where 1 < q< ∞ and Ω is a Lipschitz domain, we propose a projection method in negative Sobolev spaces W-1,p(Ω) , p being the conjugate exponent satisfying p- 1+ q- 1= 1. Our method is particularly useful when one is dealing with a rough (irregular) functional that is a member of W-1,p(Ω) , though not of L1(Ω) , but one strives for a regular approximation in L1(Ω). We focus on projections onto discrete finite element spaces Gn, and consider both discontinuous as well as continuous piecewise-polynomial approximations. While the proposed method aims to compute the best approximation as measured in the negative (dual) norm, for practical reasons, we will employ a computable, discrete dual norm that supremizes over a discrete subspace Vm. We show that this idea leads to a fully discrete method given by a mixed problem on Vm× Gn. We propose a discontinuous as well as a continuous lowest-order pair, prove that they are compatible, and therefore obtain quasi-optimally convergent methods. We present numerical experiments that compute finite element approximations to Dirac delta’s and line sources. We also present adaptively generate meshes, obtained from an error representation that comes with the method. Finally, we show how the presented projection method can be used to efficiently compute numerical approximations to partial differential equations with rough data.

AB - In order to construct regularizations of continuous linear functionals acting on Sobolev spaces such as W01,q(Ω), where 1 < q< ∞ and Ω is a Lipschitz domain, we propose a projection method in negative Sobolev spaces W-1,p(Ω) , p being the conjugate exponent satisfying p- 1+ q- 1= 1. Our method is particularly useful when one is dealing with a rough (irregular) functional that is a member of W-1,p(Ω) , though not of L1(Ω) , but one strives for a regular approximation in L1(Ω). We focus on projections onto discrete finite element spaces Gn, and consider both discontinuous as well as continuous piecewise-polynomial approximations. While the proposed method aims to compute the best approximation as measured in the negative (dual) norm, for practical reasons, we will employ a computable, discrete dual norm that supremizes over a discrete subspace Vm. We show that this idea leads to a fully discrete method given by a mixed problem on Vm× Gn. We propose a discontinuous as well as a continuous lowest-order pair, prove that they are compatible, and therefore obtain quasi-optimally convergent methods. We present numerical experiments that compute finite element approximations to Dirac delta’s and line sources. We also present adaptively generate meshes, obtained from an error representation that comes with the method. Finally, we show how the presented projection method can be used to efficiently compute numerical approximations to partial differential equations with rough data.

UR - http://www.scopus.com/inward/record.url?scp=85126803897&partnerID=8YFLogxK

U2 - 10.1007/s00211-022-01278-z

DO - 10.1007/s00211-022-01278-z

M3 - Article

AN - SCOPUS:85126803897

SN - 0029-599X

VL - 150

SP - 1087

EP - 1121

JO - Numerische Mathematik

JF - Numerische Mathematik

IS - 4

ER -