Construction of local reduced spaces for Friedrichs' systems via randomized training
<|reference_start|>Construction of local reduced spaces for Friedrichs' systems via randomized training: This contribution extends the localized training approach, traditionally employed for multiscale problems and parameterized partial differential equations (PDEs) featuring locally heterogeneous coefficients, to the class of linear, positive symmetric operators, known as Friedrichs' operators. Considering a local subdomain with corresponding oversampling domain we prove the compactness of the transfer operator which maps boundary data to solutions on the interior domain. While a Caccioppoli-inequality quantifying the energy decay to the interior holds true for all Friedrichs' systems, showing a compactness result for the graph-spaces hosting the solution is additionally necessary. We discuss the mixed formulation of a convection-diffusion-reaction problem where the necessary compactness result is obtained by the Picard-Weck-Weber theorem. Our numerical results, focusing on a scenario involving heterogeneous diffusion fields with multiple high-conductivity channels, demonstrate the effectiveness of the proposed method.<|reference_end|>
@article{engwer2024construction,
    title={Construction of local reduced spaces for Friedrichs' systems via
  randomized training},
    author={Christian Engwer, Mario Ohlberger, Lukas Renelt},
    journal={arXiv preprint arXiv:2404.18839},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18839},
    primaryClass={math.NA cs.NA}
}