Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
<|reference_start|>Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations: This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) -- also known as Scientific Machine Learning -- with particular focus to those PEML strategies developed to tackle dynamical systems' challenges. The need to go beyond Machine Learning (ML) strategies is driven by: (i) limited volume of informative data, (ii) avoiding accurate-but-wrong predictions; (iii) dealing with uncertainties; (iv) providing Explainable and Interpretable inferences. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems, are presented.<|reference_end|>
@article{cicirello2024physics-enhanced,
    title={Physics-Enhanced Machine Learning: a position paper for dynamical
  systems investigations},
    author={Alice Cicirello},
    journal={arXiv preprint arXiv:2405.05987},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05987},
    primaryClass={cs.LG cs.CE}
}