SMETANA, Kathrin; TADDEI, Tommaso

doi:10.1137/22M148402X

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SMETANA, Kathrin
Department of Electrical and Computer Engineering

TADDEI, Tommaso
Modeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
Institut de Mathématiques de Bordeaux [IMB]

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SIAM Journal on Scientific Computing. 2023-06-15, vol. 45, n° 3, p. A1300-A1331

Society for Industrial and Applied Mathematics

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We propose a component-based (CB) parametric model order reduction (pMOR) formulation for parameterized nonlinear elliptic partial differential equations (PDEs). CB-pMOR is designed to deal with large-scale problems for which full-order solves are not affordable in a reasonable time frame or parameters' variations induce topology changes that prevent the application of monolithic pMOR techniques. We rely on the partition-of-unity method (PUM) to devise global approximation spaces from local reduced spaces, and on Galerkin projection to compute the global state estimate. We propose a randomized data compression algorithm based on oversampling for the construction of the components' reduced spaces: the approach exploits random boundary conditions of controlled smoothness on the oversampling boundary. We further propose an adaptive residual-based enrichment algorithm that exploits global reduced-order solves on representative systems to update the local reduced spaces. We prove exponential convergence of the enrichment procedure for linear coercive problems; we further present numerical results for a two-dimensional nonlinear diffusion problem to illustrate the many features of our proposal and demonstrate its effectiveness.< Réduire

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parameterized partial differential equations

model order reduction

domain decomposition

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Localized model reduction for nonlinear elliptic partial differential equations: localized training, partition of unity, and adaptive enrichment

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