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Reduced order modeling for elliptic problems with high contrast diffusion coefficients

Mathematics

Reduced order modeling for elliptic problems with high contrast diffusion coefficients

A. Cohen, W. Dahmen, et al.

This groundbreaking research by Albert Cohen, Wolfgang Dahmen, Matthieu Dolbeault, and Agustin Somacal delves into reduced order modeling for parametric elliptic PDEs with high contrast diffusion coefficients. The study challenges common assumptions by exploring uniform approximation and error estimates, crucial for advancing our understanding in complex diffusion scenarios.

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~3 min • Beginner • English
Abstract
We consider a parametric elliptic PDE with a scalar piecewise constant diffusion coefficient taking arbitrary positive values on fixed subdomains. This problem is not uniformly elliptic, as the contrast can be arbitrarily high, contrarily to the Uniform Ellipticity Assumption (UEA) that is commonly made on parametric elliptic PDEs. We construct reduced model spaces that approximate uniformly well all solutions with estimates in relative error that are independent of the contrast level. These estimates are sub-exponential in the reduced model dimension, yet exhibiting the curse of dimensionality as the number of subdomains grows. Similar estimates are obtained for the Galerkin projection, as well as for the state estimation and parameter estimation inverse problems. A key ingredient in our construction and analysis is the study of the convergence towards limit solutions of stiff problems when diffusion tends to infinity in certain domains.
Publisher
arXiv preprint
Published On
Apr 24, 2023
Authors
Albert Cohen, Wolfgang Dahmen, Matthieu Dolbeault, Agustin Somacal
Tags
reduced order modeling
parametric elliptic PDEs
high contrast diffusion
uniform approximation
Galerkin projection
inverse problems
convergence analysis
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