可解释性
学习迁移
计算机科学
机器学习
人工智能
水准点(测量)
过程(计算)
领域(数学分析)
可靠性(半导体)
数学
量子力学
操作系统
大地测量学
物理
数学分析
功率(物理)
地理
作者
Alexander W. Rogers,Fernando Vega‐Ramon,Jiangtao Yan,Ehecatl Antonio del Rio‐Chanona,Keju Jing,Dongda Zhang
摘要
Predictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalized model to a more domain-specific model, provides a promising solution. While transfer learning has been used in natural language processing, image analysis, and chemical engineering fault detection, its application within biochemical engineering has not been systematically explored. In this study, we demonstrated the benefits of transfer learning when applied to predict dynamic behaviors of new biochemical processes. Two different case studies were presented to investigate the accuracy, reliability, and advantage of this innovative modeling approach. We thoroughly discussed the different transfer learning strategies and the effects of topology on transfer learning, comparing the performance of the transfer learning models against benchmark kinetic and data-driven models. Furthermore, strong connections between the underlying process mechanism and the transfer learning model's optimal structure were highlighted, suggesting the interpretability of transfer learning to enable more accurate prediction than a naive data-driven modeling approach. Therefore, this study shows a novel approach to effectively combining data from different resources for bioprocess simulation.
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