A non-intrusive data-based reformulation of a hybrid projection-based model reduction method
<|reference_start|>A non-intrusive data-based reformulation of a hybrid projection-based model reduction method: We present a novel data-driven reformulation of the iterative SVD-rational Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin (two-sided) projection-based iterative method for model reduction combining rational Krylov subspaces (on one side) with Gramian/SVD based subspaces (on the other side). We show that in each step of ISRK, we do not necessarily require access to the original system matrices, but only to input/output data in the form of the system's transfer function, evaluated at particular values (frequencies). Numerical examples illustrate the efficiency of the new data-driven formulation.<|reference_end|>
@article{gosea2024a,
    title={A non-intrusive data-based reformulation of a hybrid projection-based
  model reduction method},
    author={Ion Victor Gosea, Serkan Gugercin, Christopher Beattie},
    journal={arXiv preprint arXiv:2407.13073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13073},
    primaryClass={math.NA cs.NA math.DS}
}