Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
<|reference_start|>Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference: In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems from time-domain data. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework, to demonstrate its potential. The numerical results show the ability of the reduced-order models constructed with operator inference to provide a reduced yet accurate surrogate solution. This represents an important milestone towards the implementation of fast and reliable digital twin architectures.<|reference_end|>
@article{gosea2024learning,
    title={Learning reduced-order Quadratic-Linear models in Process Engineering
  using Operator Inference},
    author={Ion Victor Gosea, Luisa Peterson, Pawan Goyal, Jens Bremer, Kai
  Sundmacher, Peter Benner},
    journal={arXiv preprint arXiv:2402.17698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17698},
    primaryClass={math.NA cs.LG cs.NA}
}