A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes. (arXiv:2112.01475v2 [cs.LG] UPDATED)

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

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