Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study. (arXiv:2205.09196v1 [cs.LG])

To operate process engineering systems in a safe and reliable manner,
predictive models are often used in decision making. In many cases, these are
mechanistic first principles models which aim to accurately describe the
process. In practice, the parameters of these models need to be tuned to the
process conditions at hand. If the conditions change, which is common in
practice, the model becomes inaccurate and needs to be re-tuned. In this paper,
we propose a hybrid modeling machine learning …

arxiv case case study engineering flow hybrid learning machine machine learning modeling perspective study systems