TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
<|reference_start|>TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins: Purpose: Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets. Design/methodology/approach: Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively. Findings: Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed. Originality: The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.<|reference_end|>
@article{kannapinn2024twinlab:,
    title={TwinLab: a framework for data-efficient training of non-intrusive
  reduced-order models for digital twins},
    author={Maximilian Kannapinn, Michael Sch"afer, Oliver Weeger},
    journal={arXiv preprint arXiv:2407.03924},
    year={2024},
    doi={10.1108/EC-11-2023-0855},
    archivePrefix={arXiv},
    eprint={2407.03924},
    primaryClass={cs.CE cs.AI cs.SY eess.SY math.DS}
}