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Efficient localised model reduction for multiscale PDEs via Grassmannian interpolation

arXiv (Cornell University)

Abstract

Multiscale, parameter-dependent partial differential equations (PDEs) pose severe computational challenges due to strong coefficient heterogeneity and high-dimensional parameter spaces. We develop a geometric interpolation approach within the multiscale generalized finite element method (MS-GFEM) that targets the most expensive component: computing parameter-dependent optimal local approximation spaces. Leveraging the spatial localization of MS-GFEM and assuming local parameter dependence, we decompose the global problem into parametrically low-dimensional local subproblems. The optimal subspaces for each parameter are identified as points on a Grassmann manifold and approximated via Grassmann interpolation on sparse grids, which preserves the geometric structure of these spaces while efficiently handling high-dimensional parameter spaces. The resulting localized model reduction method inherits the nearly exponential spatial convergence of MS-GFEM and the parametric convergence rates of sparse grids. Numerical experiments for elliptic problems confirm the theoretical convergence results.

Authors 4

  1. Heidelberg University · Heidelberg University · Heidelberg Engineering (Germany)

    Affiliation as printed

    Institute for Mathematics , Heidelberg University , 69047 Heidelberg , Germany

  2. RWTH Aachen University

    Affiliation as printed

    Institut für Geometrie und Praktische Mathematik , RWTH Aachen University , Im Süsterfeld 2 , 52072 Aachen , Germany

  3. Heidelberg University · Heidelberg University · Heidelberg Engineering (Germany)

    Affiliation as printed

    Institute for Mathematics , Heidelberg University , 69047 Heidelberg , Germany

  4. RWTH Aachen University

    Affiliation as printed

    Institut für Geometrie und Praktische Mathematik , RWTH Aachen University , Im Süsterfeld 2 , 52072 Aachen , Germany

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