Web: http://arxiv.org/abs/2112.01475

Jan. 26, 2022, 2:11 a.m. | Niket Sharma, Y. A. Liu

cs.LG updates on arXiv.org arxiv.org

This study presents a broad perspective of hybrid process modeling and
optimization combining the scientific knowledge and data analytics in
bioprocessing and chemical engineering with a science-guided machine learning
(SGML) approach. We divide the approach into two major categories. The first
refers to the case where a data-based ML model compliments and makes the
first-principle science-based model more accurate in prediction, and the second
corresponds to the case where scientific knowledge helps make the ML model more
scientifically consistent. We …

arxiv hybrid learning machine machine learning modeling processes science

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