Linearized localized orthogonal decomposition for quasilinear nonmonotone elliptic PDE

In this paper, we propose and analyze a multiscale method for a class of quasilinear elliptic problems of nonmonotone type with spatially multiscale coefficient. The numerical approach is inspired by the Localized Orthogonal Decomposition (LOD), so that we do not require structural assumptions such as periodicity or scale separation and only need minimal regularity assumptions on the coefficient. To construct the multiscale space, we solve linear fine-scale problems on small local subdomains, for which we consider two different linearization techniques. For both, we present a rigorous well-posedness analysis and convergence estimates in the H 1 -semi norm. We compare and discuss theoretically and numerically the performance of our strategies for different linearization points. Both numerical experiments and theoretical analysis demonstrate the validity and applicability of the method.

Authors

Institutions

Publication Details

Journal
Computer Methods in Applied Mechanics and Engineering
Published
2025-09-30
DOI
https://doi.org/10.1016/j.cma.2025.118426
Primary Topic
Advanced Mathematical Modeling in Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Linearized localized orthogonal decomposition for quasilinear nonmonotone elliptic PDE

Barbara Verfürth, Maher Khrais
Computer Methods in Applied Mechanics and Engineering
Advanced Mathematical Modeling in Engineering
article

Linearized localized orthogonal decomposition for quasilinear nonmonotone elliptic PDE

Barbara Verfürth, Maher Khrais
article en

Abstract

In this paper, we propose and analyze a multiscale method for a class of quasilinear elliptic problems of nonmonotone type with spatially multiscale coefficient. The numerical approach is inspired by the Localized Orthogonal Decomposition (LOD), so that we do not require structural assumptions such as periodicity or scale separation and only need minimal regularity assumptions on the coefficient. To construct the multiscale space, we solve linear fine-scale problems on small local subdomains, for which we consider two different linearization techniques. For both, we present a rigorous well-posedness analysis and convergence estimates in the H 1 -semi norm. We compare and discuss theoretically and numerically the performance of our strategies for different linearization points. Both numerical experiments and theoretical analysis demonstrate the validity and applicability of the method.

Computer Methods in Applied Mechanics and EngineeringVol. 448
University of Bonn (DE)
Openalex Percentile: Top 72%
Advanced Mathematical Modeling in Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.